{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfiwWBvZMOB3"
      },
      "source": [
        "# **An Introduction to PROTACs**\n",
        "\n",
        "\n",
        "### David Zhang and David Figueroa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Db4WBmdAt0oL"
      },
      "source": [
        "PROTACs represent an emerging wave of new therapeutics capable of modulating proteins once thought nearly impossible to target. By eliminating rather than merely inhibiting disease-causing proteins, PROTACs address the limitations of existing drug modalities. As PROTACs progress through clinical trials with promising results, there is growing anticipation surrounding their therapeutic potential.\n",
        "\n",
        "This DeepChem tutorial serves as a starting point for exploring the world of PROTACs and the exciting field of targeted protein degradation. The tutorial is divided into five partitions:\n",
        "1. Background literature\n",
        "2. Data extraction\n",
        "3. Featurization\n",
        "4. Model deployment\n",
        "5. References\n",
        "\n",
        "With that in mind, let's jump into how we can predict efficacy of PROTAC degraders!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRty23V43quR"
      },
      "source": [
        "## 1. Background literature\n",
        "\n",
        "Traditional drug modalities, such as small-molecule drugs or monoclonal antibodies, are limited to certain modes of action, like targeting specific receptors or blocking particular pathways. Targeted protein degradation (TPD) represents a promising new approach to modulate proteins that have been traditionally difficult to target. TPD has given rise to major classes of molecules that have emerged as promising therapeutic approaches against various disease contexts.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcSMEouMPhwe"
      },
      "source": [
        "### 1.1 Targeted protein degradation\n",
        "\n",
        "Targeted protein degradation represents a way of leveraging the cell's natural degradation mechanisms to target disease causing proteins. Typically, the cell maintains protein homeostasis through clearance by proteasomes or lysosomes. By leveraging these intrinsic cellular mechanisms, TPD methods can target a variety of proteins throughout the cell. This has given rise to a collection of TPD methods aimed at degrading proteins that may play roles across many disease states. One of these major class of molecules is proteolysis-targeting chimera (PROTACs).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlR0JZyU4PVe"
      },
      "source": [
        "### 1.2 How do PROTACs work?\n",
        "\n",
        "PROTAC molecules are ternary structures consisting of a linker, a ligand to recruit and bind to the target protein, and a ligand to recruit the E3 ubiquitin ligase. Before we dive into how PROTACs mediate this degradation mechanism, it is crucial to understand the underlying biological pathway that makes this all possible.\n",
        "\n",
        "<img src=\"https://media.springernature.com/full/springer-static/image/art%3A10.1186%2Fs12943-022-01707-5/MediaObjects/12943_2022_1707_Fig14_HTML.png?as=webp\" alt=\"protac_structures.png\" width=\"700\" height=\"550\">\n",
        "\n",
        "**Figure 1:** Molecular structure of PROTACs molecules designed to inhibit epidermal growth factor receptor (EGFR). The PROTAC linker connects the EGFR ligand and E3 ligase which are highlighted in yellow and gray, respectively [[1]](#1).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viX1_tTbPupD"
      },
      "source": [
        "#### 1.1.1 Ubiquitin Proteasome System\n",
        "\n",
        "The ubiquitin proteasome system, or UPS for short, is a crucial cellular maintenance mechanism. Ubiquitin-dependent proteolysis is a three-step process which involves ubiquitin-activating enzymes (E1), ubiquitin-conjugate enzymes (E2), and ubiquitin-protein ligases (E3). In general, E1 activates ubiquitin, priming it for transfer to E2 which interacts with E3 at which point E3 ligases are responsible for binding of the target protein substrate for subsequent ubiquitination by E2. Once the protein is tagged with a polyubiquitin chain, it is recognized by the proteasome, a large protease complex that degrades that protein into peptides.  \n",
        "\n",
        "![ups.png](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3466981/bin/ijcep0005-0726-f1.jpg)\n",
        "\n",
        "**Figure 2:** The ubiquitin proteasome system is one of the cell's internal degradation mechanism crucial for targetting dysfunctional proteins. Naturally this opens up opportunities to leverage this in a therapeutic context [[2]](#2).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a31EmOQP3le"
      },
      "source": [
        "#### 1.1.2 Connection to PROTACs\n",
        "\n",
        "The realization that the UPS could be leveraged for therapeutic purposes was initially made through early studies of viruses and plants. The underlying idea involves design of small molecules capable of recruiting the E3 ligase and inducing degradation of a protein of interest (POI). This general idea naturally extended itself to the case of PROTACs. Together, the POI ligand, linker, and E3 ligase ligand make up the PROTAC complex responsible for protein degradation. Note that the presence of two ligands enables simultaneous  recruitment of the E3 ligase and POI, hence its heterobifunctionality property.\n",
        "\n",
        "Furthermore, after the POI is degraded by the proteasome, PROTACs can disassociate and continue to induce further degradation, enabling low concentrations to be efficacious. This catalytic mechanism of action and event-drive pharmacology prevents PROTACs from suffering the same limitations as conventional therapeutic strategies such as drug resistance and off-target effects.\n",
        "\n",
        "![protac.png](https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41392-019-0101-6/MediaObjects/41392_2019_101_Fig1_HTML.png?as=webp)\n",
        "\n",
        "**Figure 3:** The mechanism of action of PROTACs center around the UPS. In a heterobifunctional manner, recruiting both a target protein of interest and an E3 ligase, PROTACs are able to promote protein degradation in diseases [[3]](#3)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHvIKuq1-vo7"
      },
      "source": [
        "### 1.3 Molecular Glues\n",
        "\n",
        "It is worth noting that PROTACs are not the only TPD method. Another major class which which also leverages the UPS to elicit degradation are molecular glues. As implied by its name, molecular glues stabilize protein-protein interactions between the target protein and E3 ligase. Notice how this is different than PROTACs which consists of two separate binding ligands connected by a linker. Consequently, molecular glues may be less sterically hindered without the need for a linker. However, identifying binding sites to induce new protein-protein interactions is typically harder than accomodating for existing ligands, as in the case for PROTACs, making it harder to design molecular glues.\n",
        "\n",
        "\n",
        "<img src=\"https://ars.els-cdn.com/content/image/1-s2.0-S0960894X18303585-fx1_lrg.jpg\" alt=\"protac_structures.png\" width=\"750\" height=\"400\">\n",
        "\n",
        "\n",
        "**Figure 4:** Lenalidomide is a molecular glue which mediates the interaction between CRBN, an E3 ligase, and CK1α, resulting in subsequent ubiquitination. [[4]](#4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYSEBoaq4VBr"
      },
      "source": [
        "### 1.4 How can we leverage machine learning?\n",
        "\n",
        "As a novel and promising technique, PROTACs have demonstrated positive clinical results thus far. However, much of the clinical validation has been against classically drugged targets. In order for PROTACs to reach their full potential, their clinical efficacy against novel or hard to reach targets must be demonstrated. Consequently, there has been growing research in designing PROTAC molecules capable of elicting an effective response. However, much of the current work is empirical and requires extensive trial-and-error processes. Machine learning could potentially revolutionize this. By correlating molecular structure with physiochemical properties and biological activity, we could potentially streamline the discovery process, significantly reducing the time and cost associated with validation. With that in mind, let's jump into this tutorial to predict efficacy of PROTAC degraders!\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YG680bKL32NR"
      },
      "source": [
        "For a more in-depth dive into PROTACs, ubiquitin proteasome system, and targeted protein degradation, readers are referred to [[5]](#5) and [[6]](#6).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaFJJ8ZeLnq6"
      },
      "source": [
        "## 2. Data extraction\n",
        "\n",
        "Before we proceed, let's install deepchem into our colab environment.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZwTzQcVWlNd",
        "outputId": "c9b2f090-532d-460c-9342-d8f38205e56a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting deepchem\n",
            "  Downloading deepchem-2.8.0-py3-none-any.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.4.2)\n",
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            "Requirement already satisfied: scipy>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.11.4)\n",
            "Collecting rdkit (from deepchem)\n",
            "  Downloading rdkit-2023.9.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.9/34.9 MB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->deepchem) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->deepchem) (2023.4)\n",
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            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit->deepchem) (9.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->deepchem) (3.5.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->deepchem) (1.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->deepchem) (1.16.0)\n",
            "Installing collected packages: rdkit, deepchem\n",
            "Successfully installed deepchem-2.8.0 rdkit-2023.9.6\n"
          ]
        }
      ],
      "source": [
        "!pip install deepchem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDcpzvIUWlfh"
      },
      "source": [
        "Now let's download this dataset on PROTACs, curated by [[7]](#7), which includes 3270 PROTACs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "IQ69wZfkry9e"
      },
      "outputs": [],
      "source": [
        "# Python library imports\n",
        "import os\n",
        "\n",
        "# DeepChem and scientific library imports\n",
        "import deepchem as dc\n",
        "import rdkit\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "\n",
        "# Third-party library imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0B31rgmOuWSd",
        "outputId": "7a3b4ff2-f6c4-4c0b-de10-334dda99bbca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.system('wget https://deepchemdata.s3.us-west-1.amazonaws.com/datasets/protac_10_06_24.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "naC_T0Z7uhwi"
      },
      "outputs": [],
      "source": [
        "protac_db = pd.read_csv('protac_10_06_24.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cfrm8J40u1GL"
      },
      "source": [
        "Note that there exists a many-to-many mapping between PROTAC compounds and target proteins.\n",
        "A single PROTAC compound can be designed to target multiple proteins, and conversely, multiple PROTAC compounds can be developed to target the same protein.\n",
        "This many-to-many relationship allows for greater flexibility and adaptability in the design and application of PROTACs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "IW4PCum1N8sd",
        "outputId": "c741aca3-a381-4b1d-d5e3-f7a052fc4b23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "In this dataset, there are 3270 unique PROTAC compounds, targeting 323 unique proteins for a total of 5388 combinations\n"
          ]
        },
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5383</th>\n",
              "      <td>3266</td>\n",
              "      <td>O60885</td>\n",
              "      <td>BRD4</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CC1=C(C)C2=C(S1)N1C(C)=NN=C1[C@H](CC(=O)NCCOCC...</td>\n",
              "      <td>1600</td>\n",
              "      <td>80</td>\n",
              "      <td>Degradation of BRD4 in HEK293T cells after 8 h...</td>\n",
              "      <td>...</td>\n",
              "      <td>3.28</td>\n",
              "      <td>64</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>194.76</td>\n",
              "      <td>C44H53Cl2N9O8S</td>\n",
              "      <td>InChI=1S/C44H53Cl2N9O8S/c1-29-30(2)64-44-41(29...</td>\n",
              "      <td>UUCUKSPUFPMKNK-DHUJRADRSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5384</th>\n",
              "      <td>3267</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BCR-ABL</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CC1=NC(NC2=NC=C(C(=O)NC3=C(C)C=CC=C3Cl)S2)=CC(...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>5.92</td>\n",
              "      <td>58</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>171.95</td>\n",
              "      <td>C40H47Cl2N11O4S</td>\n",
              "      <td>InChI=1S/C40H47Cl2N11O4S/c1-27-8-6-9-30(42)38(...</td>\n",
              "      <td>WIXPXNLUXBZCHT-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5385</th>\n",
              "      <td>3268</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BCR-ABL</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CC1=NC(NC2=NC=C(C(=O)NC3=C(C)C=CC=C3Cl)S2)=CC(...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>4.41</td>\n",
              "      <td>64</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>3</td>\n",
              "      <td>23</td>\n",
              "      <td>199.64</td>\n",
              "      <td>C43H53Cl2N11O7S</td>\n",
              "      <td>InChI=1S/C43H53Cl2N11O7S/c1-30-5-3-6-33(45)41(...</td>\n",
              "      <td>YPMQMBLMNGXVCK-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5386</th>\n",
              "      <td>3269</td>\n",
              "      <td>P03372</td>\n",
              "      <td>ER</td>\n",
              "      <td>CRBN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ARV-471</td>\n",
              "      <td>O=C1CC[C@H](N2CC3=CC(N4CCN(CC5CCN(C6=CC=C([C@@...</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Degradation of ER in ER-positive breast cancer...</td>\n",
              "      <td>...</td>\n",
              "      <td>6.36</td>\n",
              "      <td>54</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>96.43</td>\n",
              "      <td>C45H49N5O4</td>\n",
              "      <td>InChI=1S/C45H49N5O4/c51-37-12-15-39-33(27-37)8...</td>\n",
              "      <td>TZZDVPMABRWKIZ-XMOGEVODSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5387</th>\n",
              "      <td>3270</td>\n",
              "      <td>P10275</td>\n",
              "      <td>AR</td>\n",
              "      <td>CRBN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ARV-110</td>\n",
              "      <td>N#CC1=CC=C(O[C@H]2CC[C@H](NC(=O)C3=CC=C(N4CCC(...</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Degradation of AR in LNCaP cells</td>\n",
              "      <td>...</td>\n",
              "      <td>3.94</td>\n",
              "      <td>58</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>181.17</td>\n",
              "      <td>C41H43ClFN9O6</td>\n",
              "      <td>InChI=1S/C41H43ClFN9O6/c42-31-19-28(4-1-25(31)...</td>\n",
              "      <td>CLCTZVRHDOAUGJ-SYVGMNBRSA-N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5388 rows × 89 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('protac_db')\"\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      Compound ID Uniprot   Target E3 ligase  PDB     Name  \\\n",
              "0               1  Q9NPI1     BRD7       VHL  NaN      NaN   \n",
              "1               1  Q9H8M2     BRD9       VHL  NaN      NaN   \n",
              "2               2  Q9NPI1     BRD7       VHL  NaN      NaN   \n",
              "3               2  Q9H8M2     BRD9       VHL  NaN      NaN   \n",
              "4               3  Q9H8M2     BRD9      CRBN  NaN      NaN   \n",
              "...           ...     ...      ...       ...  ...      ...   \n",
              "5383         3266  O60885     BRD4     FEM1B  NaN      NaN   \n",
              "5384         3267     NaN  BCR-ABL     FEM1B  NaN      NaN   \n",
              "5385         3268     NaN  BCR-ABL     FEM1B  NaN      NaN   \n",
              "5386         3269  P03372       ER      CRBN  NaN  ARV-471   \n",
              "5387         3270  P10275       AR      CRBN  NaN  ARV-110   \n",
              "\n",
              "                                                 Smiles DC50 (nM) Dmax (%)  \\\n",
              "0     COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       NaN      NaN   \n",
              "1     COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       NaN      NaN   \n",
              "2     COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       NaN      NaN   \n",
              "3     COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       NaN      NaN   \n",
              "4     COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       NaN      NaN   \n",
              "...                                                 ...       ...      ...   \n",
              "5383  CC1=C(C)C2=C(S1)N1C(C)=NN=C1[C@H](CC(=O)NCCOCC...      1600       80   \n",
              "5384  CC1=NC(NC2=NC=C(C(=O)NC3=C(C)C=CC=C3Cl)S2)=CC(...       NaN      NaN   \n",
              "5385  CC1=NC(NC2=NC=C(C(=O)NC3=C(C)C=CC=C3Cl)S2)=CC(...       NaN      NaN   \n",
              "5386  O=C1CC[C@H](N2CC3=CC(N4CCN(CC5CCN(C6=CC=C([C@@...         2      NaN   \n",
              "5387  N#CC1=CC=C(O[C@H]2CC[C@H](NC(=O)C3=CC=C(N4CCC(...         1      NaN   \n",
              "\n",
              "                                      Assay (DC50/Dmax)  ... XLogP3  \\\n",
              "0                                                   NaN  ...   3.03   \n",
              "1                                                   NaN  ...   3.03   \n",
              "2                                                   NaN  ...   2.74   \n",
              "3                                                   NaN  ...   2.74   \n",
              "4                                                   NaN  ...   0.70   \n",
              "...                                                 ...  ...    ...   \n",
              "5383  Degradation of BRD4 in HEK293T cells after 8 h...  ...   3.28   \n",
              "5384                                                NaN  ...   5.92   \n",
              "5385                                                NaN  ...   4.41   \n",
              "5386  Degradation of ER in ER-positive breast cancer...  ...   6.36   \n",
              "5387                   Degradation of AR in LNCaP cells  ...   3.94   \n",
              "\n",
              "     Heavy Atom Count Ring Count Hydrogen Bond Acceptor Count  \\\n",
              "0                  68          7                           15   \n",
              "1                  68          7                           15   \n",
              "2                  74          7                           17   \n",
              "3                  74          7                           17   \n",
              "4                  61          7                           15   \n",
              "...               ...        ...                          ...   \n",
              "5383               64          6                           15   \n",
              "5384               58          6                           13   \n",
              "5385               64          6                           16   \n",
              "5386               54          9                            7   \n",
              "5387               58          8                           12   \n",
              "\n",
              "     Hydrogen Bond Donor Count Rotatable Bond Count  \\\n",
              "0                            3                   19   \n",
              "1                            3                   19   \n",
              "2                            3                   25   \n",
              "3                            3                   25   \n",
              "4                            3                   18   \n",
              "...                        ...                  ...   \n",
              "5383                         2                   24   \n",
              "5384                         3                   17   \n",
              "5385                         3                   23   \n",
              "5386                         2                    7   \n",
              "5387                         2                    9   \n",
              "\n",
              "     Topological Polar Surface Area Molecular Formula  \\\n",
              "0                            189.92       C50H64N8O9S   \n",
              "1                            189.92       C50H64N8O9S   \n",
              "2                            208.38      C54H72N8O11S   \n",
              "3                            208.38      C54H72N8O11S   \n",
              "4                            202.97       C43H50N8O10   \n",
              "...                             ...               ...   \n",
              "5383                         194.76    C44H53Cl2N9O8S   \n",
              "5384                         171.95   C40H47Cl2N11O4S   \n",
              "5385                         199.64   C43H53Cl2N11O7S   \n",
              "5386                          96.43        C45H49N5O4   \n",
              "5387                         181.17     C41H43ClFN9O6   \n",
              "\n",
              "                                                  InChI  \\\n",
              "0     InChI=1S/C50H64N8O9S/c1-32-45(68-31-53-32)34-1...   \n",
              "1     InChI=1S/C50H64N8O9S/c1-32-45(68-31-53-32)34-1...   \n",
              "2     InChI=1S/C54H72N8O11S/c1-36-49(74-35-57-36)38-...   \n",
              "3     InChI=1S/C54H72N8O11S/c1-36-49(74-35-57-36)38-...   \n",
              "4     InChI=1S/C43H50N8O10/c1-48-24-31(28-9-10-44-23...   \n",
              "...                                                 ...   \n",
              "5383  InChI=1S/C44H53Cl2N9O8S/c1-29-30(2)64-44-41(29...   \n",
              "5384  InChI=1S/C40H47Cl2N11O4S/c1-27-8-6-9-30(42)38(...   \n",
              "5385  InChI=1S/C43H53Cl2N11O7S/c1-30-5-3-6-33(45)41(...   \n",
              "5386  InChI=1S/C45H49N5O4/c51-37-12-15-39-33(27-37)8...   \n",
              "5387  InChI=1S/C41H43ClFN9O6/c42-31-19-28(4-1-25(31)...   \n",
              "\n",
              "                        InChI Key  \n",
              "0     RPMQBLMPGMFXLD-PDUNVWSESA-N  \n",
              "1     RPMQBLMPGMFXLD-PDUNVWSESA-N  \n",
              "2     NGWWVKZONFCNQP-SHPBXJAASA-N  \n",
              "3     NGWWVKZONFCNQP-SHPBXJAASA-N  \n",
              "4     RMBNUDOJPQLHMV-UHFFFAOYSA-N  \n",
              "...                           ...  \n",
              "5383  UUCUKSPUFPMKNK-DHUJRADRSA-N  \n",
              "5384  WIXPXNLUXBZCHT-UHFFFAOYSA-N  \n",
              "5385  YPMQMBLMNGXVCK-UHFFFAOYSA-N  \n",
              "5386  TZZDVPMABRWKIZ-XMOGEVODSA-N  \n",
              "5387  CLCTZVRHDOAUGJ-SYVGMNBRSA-N  \n",
              "\n",
              "[5388 rows x 89 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('''In this dataset, there are {} unique PROTAC compounds, targeting {} unique proteins for a total of {} combinations'''.format(len(protac_db['Compound ID'].unique()),\n",
        "                                                                                   len(protac_db['Target'].unique()), protac_db.shape[0]))\n",
        "protac_db"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMoQqVzKO_h_"
      },
      "source": [
        "Taking a closer look at the dataset, each PROTAC compound has a SMILEs representation along with its target protein of interest and E3 ligase. For reference, here is an example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYCB2-Zlu-02",
        "outputId": "44d6894c-cb37-4807-e102-9ad6a63e9763"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Here is the SMILEs representation of a PROTAC compound: COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN1CCN(CCOCCOCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCC2=CC=C(C3=C(C)N=CS3)C=C2)C(C)(C)C)CC1 \n",
            "designed to target BRD7 protein through ubiquitination by VHL E3 ligase.\n"
          ]
        }
      ],
      "source": [
        "example = protac_db.iloc[0]\n",
        "print('''Here is the SMILEs representation of a PROTAC compound: {}\n",
        "designed to target {} protein through ubiquitination by {} E3 ligase.'''.format(example['Smiles'], example['Target'], example['E3 ligase']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXs5N5o9PzWs",
        "outputId": "f03900d9-df87-4c1e-bbec-f919e2e35c67"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Compound ID', 'Uniprot', 'Target', 'E3 ligase', 'PDB', 'Name',\n",
              "       'Smiles', 'DC50 (nM)', 'Dmax (%)', 'Assay (DC50/Dmax)',\n",
              "       'Percent degradation (%)', 'Assay (Percent degradation)',\n",
              "       'IC50 (nM, Protac to Target)', 'Assay (Protac to Target, IC50)',\n",
              "       'EC50 (nM, Protac to Target)', 'Assay (Protac to Target, EC50)',\n",
              "       'Kd (nM, Protac to Target)', 'Assay (Protac to Target, Kd)',\n",
              "       'Ki (nM, Protac to Target)', 'Assay (Protac to Target, Ki)',\n",
              "       'delta G (kcal/mol, Protac to Target)',\n",
              "       'delta H (kcal/mol, Protac to Target)',\n",
              "       '-T*delta S (kcal/mol, Protac to Target)',\n",
              "       'Assay (Protac to Target, G/H/-TS)', 'kon (1/Ms, Protac to Target)',\n",
              "       'koff (1/s, Protac to Target)', 't1/2 (s, Protac to Target)',\n",
              "       'Assay (Protac to Target, kon/koff/t1/2)', 'IC50 (nM, Protac to E3)',\n",
              "       'Assay (Protac to E3, IC50)', 'EC50 (nM, Protac to E3)',\n",
              "       'Assay (Protac to E3, EC50)', 'Kd (nM, Protac to E3)',\n",
              "       'Assay (Protac to E3, Kd)', 'Ki (nM, Protac to E3)',\n",
              "       'Assay (Protac to E3, Ki)', 'delta G (kcal/mol, Protac to E3)',\n",
              "       'delta H (kcal/mol, Protac to E3)',\n",
              "       '-T*delta S (kcal/mol, Protac to E3)', 'Assay (Protac to E3, G/H/-TS)',\n",
              "       'kon (1/Ms, Protac to E3)', 'koff (1/s, Protac to E3)',\n",
              "       't1/2 (s, Protac to E3)', 'Assay (Protac to E3, kon/koff/t1/2)',\n",
              "       'IC50 (nM, Ternary complex)', 'Assay (Ternary complex, IC50)',\n",
              "       'EC50 (nM, Ternary complex)', 'Assay (Ternary complex, EC50)',\n",
              "       'Kd (nM, Ternary complex)', 'Assay (Ternary complex, Kd)',\n",
              "       'Ki (nM, Ternary complex)', 'Assay (Ternary complex, Ki)',\n",
              "       'delta G (kcal/mol, Ternary complex)',\n",
              "       'delta H (kcal/mol, Ternary complex)',\n",
              "       '-T*delta S (kcal/mol, Ternary complex)',\n",
              "       'Assay (Ternary complex, G/H/-TS)', 'kon (1/Ms, Ternary complex)',\n",
              "       'koff (1/s, Ternary complex)', 't1/2 (s, Ternary complex)',\n",
              "       'Assay (Ternary complex, kon/koff/t1/2)',\n",
              "       'IC50 (nM, Cellular activities)', 'Assay (Cellular activities, IC50)',\n",
              "       'EC50 (nM, Cellular activities)', 'Assay (Cellular activities, EC50)',\n",
              "       'GI50 (nM, Cellular activities)', 'Assay (Cellular activities, GI50)',\n",
              "       'ED50 (nM, Cellular activities)', 'Assay (Cellular activities, ED50)',\n",
              "       'GR50 (nM, Cellular activities)', 'Assay (Cellular activities, GR50)',\n",
              "       'PAMPA Papp (nm/s, Permeability)', 'Assay (Permeability, PAMPA Papp)',\n",
              "       'Caco-2 A2B Papp (nm/s, Permeability)',\n",
              "       'Assay (Permeability, Caco-2 A2B Papp)',\n",
              "       'Caco-2 B2A Papp (nm/s, Permeability)',\n",
              "       'Assay (Permeability, Caco-2 B2A Papp)', 'Article DOI',\n",
              "       'Molecular Weight', 'Exact Mass', 'XLogP3', 'Heavy Atom Count',\n",
              "       'Ring Count', 'Hydrogen Bond Acceptor Count',\n",
              "       'Hydrogen Bond Donor Count', 'Rotatable Bond Count',\n",
              "       'Topological Polar Surface Area', 'Molecular Formula', 'InChI',\n",
              "       'InChI Key'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "protac_db.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmmPXNRgQgbf"
      },
      "source": [
        "In general, the PROTAC-DB dataset contains information for a variety of different physiochemical and biochemical properties of PROTAC structures. Several useful ones to point out are $ΔG$, which describes the spontaneity of a chemical reaction, $K_d$ which measures the concentration of a ligand to achieve 50% occupancy of the protein binding sites, and $XLogP3$ which measures a compound's solubility, an indication of its absorption and distribution characteristics.\n",
        "\n",
        "Before we proceed, let's plot the distribution of each of these properties to get a better sense of our PROTAC dataset starting with ΔG values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "UQrhHQVvg5im",
        "outputId": "881d147f-07a2-4d81-ce7e-2d124cec403a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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k/fORLDteObFnzx4dPXpU3bt314ULF+x1xMXFqUmTJvruu+/sP0MLFiyo7du36+zZs3e83f8qggzsYmNj0/0B1qVLF9WtW1ePP/64ihUrpq5du2rhwoWZCjUlSpSQi4tLhtuXK1fOYdxms6ls2bL2rvm75Y8//lBAQECK41GpUiX7/JuVKlUqxToKFSqU5i/Rm7dTrly5FD9M09pORi1YsEAJCQnq0qWLfdq9996rcuXKOTwpk7x/sbGxKdaxYsUKrVu3Tu+++26Gtnn16lWNHDlSJUuWlKurq3x9fVW0aFFdunRJ0dHR9nbHjx9X1apVM7TO4OBgh/Hk41GhQoUUbStWrGif7+rqqrffflurV69WsWLF1KBBA02YMEGRkZH29g0bNlTHjh01evRo+fr6qm3btoqIiFB8fHyGaitdurTWrVunb7/9Vlu2bFFkZKS+/PJLh0tjkuTm5qZ169Zp3bp1ioiIUKVKlXT+/PkUl0aT/y/S+2MiI2EnNV988YU8PDxUpkwZHTt2TMeOHZObm5tKly7tcD54e3vftoaMuvUz4ePjI0kqWbJkiulJSUn2c+SPP/5Qnjx5VLZsWYd2/v7+KliwYLqfiaNHj+rAgQMOAbxo0aIqX768JOn8+fOS/jkH8+TJo8qVK9/ZTqZTh/RPEL+1ls8++0zx8fH2/Z0wYYL279+vkiVLqlatWnrjjTd43UEmcY8MJEmnT59WdHR0ih8eN3N3d9d3332njRs36quvvtI333yjBQsW6MEHH9TatWsz9GhuZu5ryai0/oJKTEzMsceF09qOuem9Hzlpzpw5KlWqlOrUqeMwvUuXLnrzzTf122+/qUyZMipbtqzy5cun/fv3p1hHw4YNJf1zb0ZGDBw4UBERERo8eLDCwsLk4+Mjm82mrl27ZrkH707Ol8GDB6tNmzZavny51qxZo9dff13h4eHasGGD7r33XvsLF7dt26ZVq1ZpzZo16tevnyZOnKht27al2kt1Mw8Pjwzdp5I3b16Hdi1atFDFihX11FNPOfRyVapUScuXL9e+ffvUoEGDVNe1b98+ScrUL2BjjObNm6e4uLhUlzt//rxiY2Pl6empihUrSpJ++eWXTL0+ITVpfSYy+lnJSs9IUlKSqlWrpkmTJqU6/9YQlVWJiYm3rUOS3nnnnTSPY/L51blzZ9WvX1/Lli3T2rVr9c477+jtt9/W0qVL03xdAhzRIwNJ0ueffy7pnx+y6cmTJ4+aNGmiSZMm6ddff9W4ceO0YcMGe5dtdr8JOPkvm2TGGB07dszhLbyFChXSpUuXUix7619umaktKChIZ8+eTfGX6aFDh+zzs0NQUJCOHj2a4hf9nWzn999/148//qjOnTun2OfkHpq5c+dK+ueXcaNGjbR582adOXMmK7tgt3jxYvXu3VsTJ05Up06d1KxZM9WrVy/F/01ISEiqwSkjko/H4cOHU8w7fPhwiuMVEhKiYcOGae3atdq/f7+uX7+uiRMnOrSpU6eOxo0bp507d2rOnDk6cOCA5s+fn6X6MqJ48eIaMmSIVq1apW3bttmnP/zww5Kk2bNnp7pcYmKi5s6dq0KFCqlu3boZ3t7mzZt1+vRpjRkzRosWLXIYPvnkE125csV+E2zLli2VN29effHFF1nfwTsUFBSkpKSkFJ/9qKgoXbp0Kd3PREhIiP7++281adJETZs2TTEk9+SFhIQoKSlJv/76a7q1pPaz5fr16zp37ly6yyVfZvb29k61jqZNmzpcditevLieffZZLV++XL///ruKFCmicePGpbsN/D+CDLRhwwaNHTtWwcHB6tGjR5rt/v777xTTkv/aSO6O9/DwkKRUg0VWzJ492yFMLF68WOfOnXP4SyUkJETbtm3T9evX7dO+/PJLnTp1ymFdmamtVatWSkxM1P/+9z+H6e+9955sNlu2/aXUqlUrRUZGasGCBfZpN27c0IcffihPT097r0hmJF8q6Ny5c4p5VatWVeXKlR0uJ4wcOVKJiYl67LHHUr3ElNFepbx586Zo++GHH6b467Vjx47au3dvqk+f3G5b999/v/z8/DRt2jSHS0CrV6/WwYMH1bp1a0n/PI1165tzQ0JC5OXlZV/u4sWLKbZ36/l8twwcOFAFChRweHHeAw88oKZNmyoiIkJffvllimVeffVVHTlyRC+99FKmeqqSLyu9+OKL6tSpk8PwxBNPOFxuLFmypJ544gmtXbtWH374YYp1JSUlaeLEiTp9+nQW9jpjWrVqJUmaPHmyw/TkXpbk/+PUdO7cWWfOnNGnn36aYt7Vq1cVFxcnSWrXrp3y5MmjMWPGpPgj4uZzIiQkRN99953D/E8++eS2PTI1atRQSEiI3n333VQ/U3/++aekf8LpzZddJcnPz08BAQF3/Rz8N+HS0n/M6tWrdejQId24cUNRUVHasGGD1q1bp6CgIK1cudLhBspbjRkzRt99951at26toKAgnT9/XlOmTFFgYKDq1asn6Z8PfsGCBTVt2jR5eXnJw8NDtWvXTnGvQ0YVLlxY9erVU9++fRUVFaXJkyerbNmyDo+IP/7441q8eLEeeughde7cWcePH9cXX3zhcPNtZmtr06aNGjdurFdffVUnTpxQ9erVtXbtWq1YsUKDBw9Ose6sevLJJ/Xxxx+rT58+2rVrl0qXLq3Fixfrhx9+0OTJk7N00+WcOXPk6uqq9evXa/369Snm+/j4aOvWrfr555913333qX79+vrf//6ngQMHqly5cvY3+16/fl1HjhzRnDlz5OLiIn9//3S3+/DDD+vzzz+Xj4+PKleurK1bt+rbb79N8fj5iy++qMWLF+vRRx9Vv379VKNGDf39999auXKlpk2bpurVq6e5jfz58+vtt99W37591bBhQ3Xr1s3++HXp0qU1ZMgQSdKRI0fUpEkTde7cWZUrV1a+fPm0bNkyRUVFqWvXrpKkWbNmacqUKWrfvr1CQkJ0+fJlffrpp/L29rb/Mr1bihQpor59+2rKlCk6ePCg/Z6o2bNnq0mTJmrbtq26d++u+vXrKz4+XkuXLtWmTZvUpUsXvfjiixneTnx8vJYsWaJmzZql+dl+5JFH9P777+v8+fPy8/PTxIkTdfz4cT3//PNaunSpHn74YRUqVEgnT57UokWLdOjQIfsxvBuqV6+u3r1765NPPtGlS5fUsGFD/fTTT5o1a5batWunxo0bp7lsz549tXDhQj399NPauHGj6tatq8TERB06dEgLFy7UmjVrdP/996ts2bJ69dVXNXbsWNWvX18dOnSQq6urduzYoYCAAPsbmx9//HE9/fTT6tixo5o1a6a9e/dqzZo1Ke6DulWePHn02WefqWXLlqpSpYr69u2rEiVK6MyZM9q4caO8vb21atUqXb58WYGBgerUqZOqV68uT09Pffvtt9qxY0eKnkOkw0lPSyGHJT9+nTwkvyCsWbNm5v3330/1ja63Pn69fv1607ZtWxMQEGBcXFxMQECA6datmzly5IjDcitWrDCVK1c2+fLlS/WFeKlJ6/HrefPmmREjRhg/Pz/j7u5uWrdubf74448Uy0+cONGUKFHCuLq6mrp165qdO3em+rKstGpL7YV4ly9fNkOGDDEBAQEmf/78ply5cum+EO9WaT0WfquoqCjTt29f4+vra1xcXEy1atVSfYQ2I49f79q1K9WX56U2DBkyxGHZ3bt3m169eplSpUoZFxcX4+HhYUJDQ82wYcMcHi1Py8WLF+374enpaVq0aGEOHTqU6nG4cOGCee655+wvqwsMDDS9e/c2f/31lzHm9m92XrBggbn33nuNq6urKVy4cIoX4v31119mwIABpmLFisbDw8P4+PiY2rVrm4ULF9rb/Pzzz6Zbt26mVKlSxtXV1fj5+ZmHH37Y7Ny587b7mt65fLO0XohnjDHHjx83efPmTXFsLl++bN544w1TpUoV4+7ubry8vEzdunXNzJkzU5x7N0vt8eslS5YYSWb69OlpLrdp06YUrzS4ceOG+eyzz0z9+vWNj4+PyZ8/vwkKCjJ9+/a97aPZaf3fpfUKiOSfMze/1iEhIcGMHj3aBAcHm/z585uSJUtm+IV4169fN2+//bapUqWKcXV1NYUKFTI1atQwo0ePNtHR0Q5tZ8yYYT+PChUqZBo2bGjWrVtnn5+YmGiGDx9ufH19TYECBUyLFi3MsWPHMvxCvN27d5sOHTqYIkWKGFdXVxMUFGQ6d+5s1q9fb4wxJj4+3rz44oumevXqxsvLy3h4eJjq1aubKVOmpHuM4chmjJPuRgQAALhD3CMDAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAs61//QrykpCSdPXtWXl5e2f76fAAAcHcYY3T58mUFBASk+GLdm/3rg8zZs2ez7YvCAABAzjp16pQCAwPTnP+vDzLJr3g/deqU/SvqAQBA7hYTE6OSJUve9qta/vVBJvlykre3N0EGAACLud1tIdzsCwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALCvXBJnx48fLZrNp8ODB9mnXrl3TgAEDVKRIEXl6eqpjx46KiopyXpEAACBXyRVBZseOHfr4448VGhrqMH3IkCFatWqVFi1apM2bN+vs2bPq0KGDk6oEAAC5jdODTGxsrHr06KFPP/1UhQoVsk+Pjo7W9OnTNWnSJD344IOqUaOGIiIi9OOPP2rbtm1OrBgAAOQWTg8yAwYMUOvWrdW0aVOH6bt27VJCQoLD9IoVK6pUqVLaunVrTpcJAAByoXzO3Pj8+fP1888/a8eOHSnmRUZGysXFRQULFnSYXqxYMUVGRqa5zvj4eMXHx9vHY2Jisq1eAACQuzgtyJw6dUqDBg3SunXr5Obmlm3rDQ8P1+jRo7NtfQAA5JTSL3/l7BIy7cT41k7dvtMuLe3atUvnz5/Xfffdp3z58ilfvnzavHmzPvjgA+XLl0/FihXT9evXdenSJYfloqKi5O/vn+Z6R4wYoejoaPtw6tSpu7wnAADAWZzWI9OkSRP98ssvDtP69u2rihUravjw4SpZsqTy58+v9evXq2PHjpKkw4cP6+TJkwoLC0tzva6urnJ1db2rtQMAgNzBaUHGy8tLVatWdZjm4eGhIkWK2Kf3799fQ4cOVeHCheXt7a2BAwcqLCxMderUcUbJAAAgl3Hqzb6389577ylPnjzq2LGj4uPj1aJFC02ZMsXZZQEAgFzCZowxzi7iboqJiZGPj4+io6Pl7e3t7HIAAEgTN/v+v4z+/nb6e2QAAACyiiADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsy6lBZurUqQoNDZW3t7e8vb0VFham1atX2+c3atRINpvNYXj66aedWDEAAMhN8jlz44GBgRo/frzKlSsnY4xmzZqltm3bavfu3apSpYok6YknntCYMWPsyxQoUMBZ5QIAgFzGqUGmTZs2DuPjxo3T1KlTtW3bNnuQKVCggPz9/Z1RHgAAyOVyzT0yiYmJmj9/vuLi4hQWFmafPmfOHPn6+qpq1aoaMWKErly54sQqAQBAbuLUHhlJ+uWXXxQWFqZr167J09NTy5YtU+XKlSVJ3bt3V1BQkAICArRv3z4NHz5chw8f1tKlS9NcX3x8vOLj4+3jMTExd30fAACAczg9yFSoUEF79uxRdHS0Fi9erN69e2vz5s2qXLmynnzySXu7atWqqXjx4mrSpImOHz+ukJCQVNcXHh6u0aNH51T5AADAiZx+acnFxUVly5ZVjRo1FB4erurVq+v9999PtW3t2rUlSceOHUtzfSNGjFB0dLR9OHXq1F2pGwAAOJ/Te2RulZSU5HBp6GZ79uyRJBUvXjzN5V1dXeXq6no3SgMAALmMU4PMiBEj1LJlS5UqVUqXL1/W3LlztWnTJq1Zs0bHjx/X3Llz1apVKxUpUkT79u3TkCFD1KBBA4WGhjqzbAAAkEs4NcicP39evXr10rlz5+Tj46PQ0FCtWbNGzZo106lTp/Ttt99q8uTJiouLU8mSJdWxY0e99tprziwZAADkIk4NMtOnT09zXsmSJbV58+YcrAYAAFiN02/2BQAAyCqCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCynBpmpU6cqNDRU3t7e8vb2VlhYmFavXm2ff+3aNQ0YMEBFihSRp6enOnbsqKioKCdWDAAAchOnBpnAwECNHz9eu3bt0s6dO/Xggw+qbdu2OnDggCRpyJAhWrVqlRYtWqTNmzfr7Nmz6tChgzNLBgAAuYjNGGOcXcTNChcurHfeeUedOnVS0aJFNXfuXHXq1EmSdOjQIVWqVElbt25VnTp1MrS+mJgY+fj4KDo6Wt7e3nezdAAA7kjpl79ydgmZdmJ867uy3oz+/s4198gkJiZq/vz5iouLU1hYmHbt2qWEhAQ1bdrU3qZixYoqVaqUtm7d6sRKAQBAbpHP2QX88ssvCgsL07Vr1+Tp6ally5apcuXK2rNnj1xcXFSwYEGH9sWKFVNkZGSa64uPj1d8fLx9PCYm5m6VDgAAnMzpPTIVKlTQnj17tH37dj3zzDPq3bu3fv311yyvLzw8XD4+PvahZMmS2VgtAADITZweZFxcXFS2bFnVqFFD4eHhql69ut5//335+/vr+vXrunTpkkP7qKgo+fv7p7m+ESNGKDo62j6cOnXqLu8BAABwFqcHmVslJSUpPj5eNWrUUP78+bV+/Xr7vMOHD+vkyZMKCwtLc3lXV1f749zJAwAA+Hdy6j0yI0aMUMuWLVWqVCldvnxZc+fO1aZNm7RmzRr5+Piof//+Gjp0qAoXLixvb28NHDhQYWFhGX5iCQAA/Ls5NcicP39evXr10rlz5+Tj46PQ0FCtWbNGzZo1kyS99957ypMnjzp27Kj4+Hi1aNFCU6ZMcWbJAAAgF8l175HJbrxHBgBgFbxH5v9Z7j0yAAAAmUWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAluXUIBMeHq6aNWvKy8tLfn5+ateunQ4fPuzQplGjRrLZbA7D008/7aSKAQBAbuLUILN582YNGDBA27Zt07p165SQkKDmzZsrLi7Ood0TTzyhc+fO2YcJEyY4qWIAAJCb5HPmxr/55huH8ZkzZ8rPz0+7du1SgwYN7NMLFCggf3//nC4PAADkcrnqHpno6GhJUuHChR2mz5kzR76+vqpatapGjBihK1euOKM8AACQyzi1R+ZmSUlJGjx4sOrWrauqVavap3fv3l1BQUEKCAjQvn37NHz4cB0+fFhLly5NdT3x8fGKj4+3j8fExNz12gEAgHPkmiAzYMAA7d+/X1u2bHGY/uSTT9r/Xa1aNRUvXlxNmjTR8ePHFRISkmI94eHhGj169F2vFwAAOF+uuLT03HPP6csvv9TGjRsVGBiYbtvatWtLko4dO5bq/BEjRig6Oto+nDp1KtvrBQAAuUOWemR+++03lSlT5o43bozRwIEDtWzZMm3atEnBwcG3XWbPnj2SpOLFi6c639XVVa6urndcGwAAyP2y1CNTtmxZNW7cWF988YWuXbuW5Y0PGDBAX3zxhebOnSsvLy9FRkYqMjJSV69elSQdP35cY8eO1a5du3TixAmtXLlSvXr1UoMGDRQaGprl7QIAgH+HLAWZn3/+WaGhoRo6dKj8/f311FNP6aeffsr0eqZOnaro6Gg1atRIxYsXtw8LFiyQJLm4uOjbb79V8+bNVbFiRQ0bNkwdO3bUqlWrslI2AAD4l7EZY0xWF75x44ZWrlypmTNn6ptvvlH58uXVr18/9ezZU0WLFs3OOrMsJiZGPj4+io6Olre3t7PLAQAgTaVf/srZJWTaifGt78p6M/r7+45u9s2XL586dOigRYsW6e2339axY8f0wgsvqGTJkurVq5fOnTt3J6sHAABI1x0FmZ07d+rZZ59V8eLFNWnSJL3wwgs6fvy41q1bp7Nnz6pt27bZVScAAEAKWXpqadKkSYqIiNDhw4fVqlUrzZ49W61atVKePP/kouDgYM2cOVOlS5fOzloBAAAcZCnITJ06Vf369VOfPn3SfAzaz89P06dPv6PiAAAA0pOlIHP06NHbtnFxcVHv3r2zsnoAAIAMydI9MhEREVq0aFGK6YsWLdKsWbPuuCgAAICMyFKQCQ8Pl6+vb4rpfn5+euutt+64KAAAgIzIUpA5efJkql8nEBQUpJMnT95xUQAAABmRpSDj5+enffv2pZi+d+9eFSlS5I6LAgAAyIgsBZlu3brp+eef18aNG5WYmKjExERt2LBBgwYNUteuXbO7RgAAgFRl6amlsWPH6sSJE2rSpIny5ftnFUlJSerVqxf3yAAAgByTpSDj4uKiBQsWaOzYsdq7d6/c3d1VrVo1BQUFZXd9AAAAacpSkElWvnx5lS9fPrtqAQAAyJQsBZnExETNnDlT69ev1/nz55WUlOQwf8OGDdlSHAAAQHqyFGQGDRqkmTNnqnXr1qpatapsNlt21wUAAHBbWQoy8+fP18KFC9WqVavsrgcAACDDsvT4tYuLi8qWLZvdtQAAAGRKloLMsGHD9P7778sYk931AAAAZFiWLi1t2bJFGzdu1OrVq1WlShXlz5/fYf7SpUuzpTgAAID0ZCnIFCxYUO3bt8/uWgAAADIlS0EmIiIiu+sAAADItCzdIyNJN27c0LfffquPP/5Yly9fliSdPXtWsbGx2VYcAABAerLUI/PHH3/ooYce0smTJxUfH69mzZrJy8tLb7/9tuLj4zVt2rTsrhMAACCFLPXIDBo0SPfff78uXrwod3d3+/T27dtr/fr12VYcAABAerLUI/P999/rxx9/lIuLi8P00qVL68yZM9lSGAAAwO1kqUcmKSlJiYmJKaafPn1aXl5ed1wUAABARmQpyDRv3lyTJ0+2j9tsNsXGxmrUqFF8bQEAAMgxWbq0NHHiRLVo0UKVK1fWtWvX1L17dx09elS+vr6aN29edtcIAACQqiwFmcDAQO3du1fz58/Xvn37FBsbq/79+6tHjx4ON/8CAADcTVkKMpKUL18+PfbYY9lZCwAAQKZkKcjMnj073fm9evXKUjEAAACZkaUgM2jQIIfxhIQEXblyRS4uLipQoABBBgAA5IgsPbV08eJFhyE2NlaHDx9WvXr1uNkXAADkmCx/19KtypUrp/Hjx6forUlPeHi4atasKS8vL/n5+aldu3Y6fPiwQ5tr165pwIABKlKkiDw9PdWxY0dFRUVlV9kAAMDCsi3ISP/cAHz27NkMt9+8ebMGDBigbdu2ad26dUpISFDz5s0VFxdnbzNkyBCtWrVKixYt0ubNm3X27Fl16NAhO8sGAAAWlaV7ZFauXOkwbozRuXPn9L///U9169bN8Hq++eYbh/GZM2fKz89Pu3btUoMGDRQdHa3p06dr7ty5evDBByVJERERqlSpkrZt26Y6depkpXwAAPAvkaUg065dO4dxm82mokWL6sEHH9TEiROzXEx0dLQkqXDhwpKkXbt2KSEhQU2bNrW3qVixokqVKqWtW7cSZAAA+I/LUpBJSkrK7jqUlJSkwYMHq27duqpataokKTIyUi4uLipYsKBD22LFiikyMjLV9cTHxys+Pt4+HhMTk+21AgCA3CFb75G5EwMGDND+/fs1f/78O1pPeHi4fHx87EPJkiWzqUIAAJDbZKlHZujQoRluO2nSpNu2ee655/Tll1/qu+++U2BgoH26v7+/rl+/rkuXLjn0ykRFRcnf3z/VdY0YMcKhvpiYGMIMAAD/UlkKMrt379bu3buVkJCgChUqSJKOHDmivHnz6r777rO3s9ls6a7HGKOBAwdq2bJl2rRpk4KDgx3m16hRQ/nz59f69evVsWNHSdLhw4d18uRJhYWFpbpOV1dXubq6ZmW3AACAxWQpyLRp00ZeXl6aNWuWChUqJOmfl+T17dtX9evX17BhwzK0ngEDBmju3LlasWKFvLy87Pe9+Pj4yN3dXT4+Purfv7+GDh2qwoULy9vbWwMHDlRYWBg3+gIAANmMMSazC5UoUUJr165VlSpVHKbv379fzZs3z/C7ZNLqsYmIiFCfPn0k/fNCvGHDhmnevHmKj49XixYtNGXKlDQvLd0qJiZGPj4+io6Olre3d4aWAQDAGUq//JWzS8i0E+Nb35X1ZvT3d5Z6ZGJiYvTnn3+mmP7nn3/q8uXLGV5PRjKUm5ubPvroI3300UeZqhEAAPz7Zemppfbt26tv375aunSpTp8+rdOnT2vJkiXq378/b90FAAA5Jks9MtOmTdMLL7yg7t27KyEh4Z8V5cun/v3765133snWAgEAANKSpSBToEABTZkyRe+8846OHz8uSQoJCZGHh0e2FgcAAJCeO3oh3rlz53Tu3DmVK1dOHh4eGbrnBQAAILtkKchcuHBBTZo0Ufny5dWqVSudO3dOktS/f/8MP3oNAABwp7IUZIYMGaL8+fPr5MmTKlCggH16ly5dUnyjNQAAwN2SpXtk1q5dqzVr1jh8nYAklStXTn/88Ue2FAYAAHA7WeqRiYuLc+iJSfb333/z9QAAACDHZCnI1K9fX7Nnz7aP22w2JSUlacKECWrcuHG2FQcAAJCeLF1amjBhgpo0aaKdO3fq+vXreumll3TgwAH9/fff+uGHH7K7RgAAgFRlqUematWqOnLkiOrVq6e2bdsqLi5OHTp00O7duxUSEpLdNQIAAKQq0z0yCQkJeuihhzRt2jS9+uqrd6MmAACADMl0j0z+/Pm1b9++u1ELAABApmTp0tJjjz2m6dOnZ3ctAAAAmZKlm31v3LihGTNm6Ntvv1WNGjVSfMfSpEmTsqU4AACA9GQqyPz2228qXbq09u/fr/vuu0+SdOTIEYc2Npst+6oDAABIR6aCTLly5XTu3Dlt3LhR0j9fSfDBBx+oWLFid6U4AACA9GTqHplbv9169erViouLy9aCAAAAMipLN/smuzXYAAAA5KRMBRmbzZbiHhjuiQEAAM6SqXtkjDHq06eP/Yshr127pqeffjrFU0tLly7NvgoBAADSkKkg07t3b4fxxx57LFuLAQAAyIxMBZmIiIi7VQcAAECm3dHNvgAAAM5EkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJbl1CDz3XffqU2bNgoICJDNZtPy5csd5vfp08f+jdvJw0MPPeScYgEAQK7j1CATFxen6tWr66OPPkqzzUMPPaRz587Zh3nz5uVghQAAIDfL1JdGZreWLVuqZcuW6bZxdXWVv79/DlUEAACsJNffI7Np0yb5+fmpQoUKeuaZZ3ThwgVnlwQAAHIJp/bI3M5DDz2kDh06KDg4WMePH9crr7yili1bauvWrcqbN2+qy8THxys+Pt4+HhMTk1PlAgCAHJarg0zXrl3t/65WrZpCQ0MVEhKiTZs2qUmTJqkuEx4ertGjR+dUiQAAwIly/aWlm5UpU0a+vr46duxYmm1GjBih6Oho+3Dq1KkcrBAAAOSkXN0jc6vTp0/rwoULKl68eJptXF1d5erqmoNVAQAAZ3FqkImNjXXoXfn999+1Z88eFS5cWIULF9bo0aPVsWNH+fv76/jx43rppZdUtmxZtWjRwolVAwCA3MKpQWbnzp1q3LixfXzo0KGSpN69e2vq1Knat2+fZs2apUuXLikgIEDNmzfX2LFj6XEBAACSnBxkGjVqJGNMmvPXrFmTg9UAAACrsdTNvgAAADcjyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMtyapD57rvv1KZNGwUEBMhms2n58uUO840xGjlypIoXLy53d3c1bdpUR48edU6xAAAg13FqkImLi1P16tX10UcfpTp/woQJ+uCDDzRt2jRt375dHh4eatGiha5du5bDlQIAgNwonzM33rJlS7Vs2TLVecYYTZ48Wa+99pratm0rSZo9e7aKFSum5cuXq2vXrjlZKgAAyIVy7T0yv//+uyIjI9W0aVP7NB8fH9WuXVtbt251YmUAACC3cGqPTHoiIyMlScWKFXOYXqxYMfu81MTHxys+Pt4+HhMTc3cKBAAATpdre2SyKjw8XD4+PvahZMmSzi4JAADcJbk2yPj7+0uSoqKiHKZHRUXZ56VmxIgRio6Otg+nTp26q3UCAADnybVBJjg4WP7+/lq/fr19WkxMjLZv366wsLA0l3N1dZW3t7fDAAAA/p2ceo9MbGysjh07Zh///ffftWfPHhUuXFilSpXS4MGD9eabb6pcuXIKDg7W66+/roCAALVr1855RQMAgFzDqUFm586daty4sX186NChkqTevXtr5syZeumllxQXF6cnn3xSly5dUr169fTNN9/Izc3NWSUDAIBcxGaMMc4u4m6KiYmRj4+PoqOjucwEAMjVSr/8lbNLyLQT41vflfVm9Pd3rr1HBgAA4HYIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLJydZB54403ZLPZHIaKFSs6uywAAJBL5HN2AbdTpUoVffvtt/bxfPlyfckAACCH5PpUkC9fPvn7+zu7DAAAkAvl6ktLknT06FEFBASoTJky6tGjh06ePOnskgAAQC6Rq3tkateurZkzZ6pChQo6d+6cRo8erfr162v//v3y8vJKdZn4+HjFx8fbx2NiYnKqXAAAkMNydZBp2bKl/d+hoaGqXbu2goKCtHDhQvXv3z/VZcLDwzV69OicKhEAADhRrr+0dLOCBQuqfPnyOnbsWJptRowYoejoaPtw6tSpHKwQAADkJEsFmdjYWB0/flzFixdPs42rq6u8vb0dBgAA8O+Uq4PMCy+8oM2bN+vEiRP68ccf1b59e+XNm1fdunVzdmkAACAXyNX3yJw+fVrdunXThQsXVLRoUdWrV0/btm1T0aJFnV0aAADIBXJ1kJk/f76zSwAAALlYrr60BAAAkB6CDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsKxc/V1LuV3pl79ydglZcmJ8a2eXAABAtqBHBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWJYlgsxHH32k0qVLy83NTbVr19ZPP/3k7JIAAEAukOuDzIIFCzR06FCNGjVKP//8s6pXr64WLVro/Pnzzi4NAAA4Wa4PMpMmTdITTzyhvn37qnLlypo2bZoKFCigGTNmOLs0AADgZLk6yFy/fl27du1S06ZN7dPy5Mmjpk2bauvWrU6sDAAA5Ab5nF1Aev766y8lJiaqWLFiDtOLFSumQ4cOpbpMfHy84uPj7ePR0dGSpJiYmGyvLyn+SravMyfcjWMBALhzVvy9crd+pySv1xiTbrtcHWSyIjw8XKNHj04xvWTJkk6oJnfymezsCgAA/xZ3+3fK5cuX5ePjk+b8XB1kfH19lTdvXkVFRTlMj4qKkr+/f6rLjBgxQkOHDrWPJyUl6e+//1aRIkVks9nuar25XUxMjEqWLKlTp07J29vb2eVYAscsczhemccxyzyOWeZY9XgZY3T58mUFBASk2y5XBxkXFxfVqFFD69evV7t27ST9E0zWr1+v5557LtVlXF1d5erq6jCtYMGCd7lSa/H29rbUyZwbcMwyh+OVeRyzzOOYZY4Vj1d6PTHJcnWQkaShQ4eqd+/euv/++1WrVi1NnjxZcXFx6tu3r7NLAwAATpbrg0yXLl30559/auTIkYqMjNQ999yjb775JsUNwAAA4L8n1wcZSXruuefSvJSEjHN1ddWoUaNSXHpD2jhmmcPxyjyOWeZxzDLn3368bOZ2zzUBAADkUrn6hXgAAADpIcgAAADLIsgAAADLIsgAAADLIsj8RzzyyCMqVaqU3NzcVLx4cfXs2VNnz561zz9x4oRsNluKYdu2bU6s2nlud7wkad++fapfv77c3NxUsmRJTZgwwUnVOt+JEyfUv39/BQcHy93dXSEhIRo1apSuX7/u0IZz7P9l5JhJnGc3GzdunB544AEVKFAgzRedpnaOzZ8/P2cLzUUycsxOnjyp1q1bq0CBAvLz89OLL76oGzdu5Gyhd8ASj1/jzjVu3FivvPKKihcvrjNnzuiFF15Qp06d9OOPPzq0+/bbb1WlShX7eJEiRXK61FzhdscrJiZGzZs3V9OmTTVt2jT98ssv6tevnwoWLKgnn3zSydXnvEOHDikpKUkff/yxypYtq/379+uJJ55QXFyc3n33XYe2nGP/yMgx4zxzdP36dT366KMKCwvT9OnT02wXERGhhx56yD7+X367++2OWWJiolq3bi1/f3/9+OOPOnfunHr16qX8+fPrrbfeckLFWWDwn7RixQpjs9nM9evXjTHG/P7770aS2b17t3MLy6VuPV5TpkwxhQoVMvHx8fY2w4cPNxUqVHBWibnOhAkTTHBwsH2cc+z2bj1mnGepi4iIMD4+PqnOk2SWLVuWo/VYQVrH7OuvvzZ58uQxkZGR9mlTp0413t7eDuddbsalpf+gv//+W3PmzNEDDzyg/PnzO8x75JFH5Ofnp3r16mnlypVOqjB3Se14bd26VQ0aNJCLi4u9XYsWLXT48GFdvHjRWaXmKtHR0SpcuHCK6Zxjabv1mHGeZc2AAQPk6+urWrVqacaMGTK8Li1NW7duVbVq1Rzelt+iRQvFxMTowIEDTqws4wgy/yHDhw+Xh4eHihQpopMnT2rFihX2eZ6enpo4caIWLVqkr776SvXq1VO7du3+079o0jtekZGRKb4mI3k8MjIyR+vMjY4dO6YPP/xQTz31lH0a51j6UjtmnGeZN2bMGC1cuFDr1q1Tx44d9eyzz+rDDz90dlm51r/iHHN2lxCybvjw4UZSusPBgwft7f/8809z+PBhs3btWlO3bl3TqlUrk5SUlOb6e/bsaerVq5cTu5IjsvN4NWvWzDz55JMO6z9w4ICRZH799dcc3a+7KbPHzBhjTp8+bUJCQkz//v1vu/5/2zlmTPYes//CeZaV45XepaVbvf766yYwMPAuVO482XnMnnjiCdO8eXOHaXFxcUaS+frrr+/mbmQbbva1sGHDhqlPnz7ptilTpoz9376+vvL19VX58uVVqVIllSxZUtu2bVNYWFiqy9auXVvr1q3LzpKdKjuPl7+/v6KiohyWTR739/fP9tqdJbPH7OzZs2rcuLEeeOABffLJJ7dd/7/tHJOy95j9F86zzB6vzKpdu7bGjh2r+Pj4f813DWXnMfP399dPP/3kMM1q5xhBxsKKFi2qokWLZmnZpKQkSVJ8fHyabfbs2aPixYtnaf25UXYer7CwML366qtKSEiw3zezbt06VahQQYUKFcqegnOBzByzM2fOqHHjxqpRo4YiIiKUJ8/tr1z/284xKXuP2X/hPLuTz2VG7NmzR4UKFfrXhBgpe49ZWFiYxo0bp/Pnz8vPz0/SP+eYt7e3KleunC3buNsIMv8B27dv144dO1SvXj0VKlRIx48f1+uvv66QkBB7b8ysWbPk4uKie++9V5K0dOlSzZgxQ5999pkzS3eKjByv7t27a/To0erfv7+GDx+u/fv36/3339d7773n5Oqd48yZM2rUqJGCgoL07rvv6s8//7TPS/6rjnPMUUaOGeeZo5MnT+rvv//WyZMnlZiYqD179kiSypYtK09PT61atUpRUVGqU6eO3NzctG7dOr311lt64YUXnFu4E93umDVv3lyVK1dWz549NWHCBEVGRuq1117TgAEDrBP+nH1tC3ffvn37TOPGjU3hwoWNq6urKV26tHn66afN6dOn7W1mzpxpKlWqZAoUKGC8vb1NrVq1zKJFi5xYtfNk5HgZY8zevXtNvXr1jKurqylRooQZP368kyp2voiIiDSv1SfjHHOUkWNmDOfZzXr37p3q8dq4caMxxpjVq1ebe+65x3h6ehoPDw9TvXp1M23aNJOYmOjcwp3odsfMGGNOnDhhWrZsadzd3Y2vr68ZNmyYSUhIcF7RmWQzhufSAACANfH4NQAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDIAcNX36dDVv3tw+3qdPH7Vr1+6ubW/Tpk2y2Wy6dOnSXdtGWho1aqTBgwfbx+vUqaMlS5bkeB3AvxlBBoCDrVu3ymazqXXr1mm2WbJkiR588EEVKlRI7u7uqlChgvr166fdu3enu+5r167p9ddf16hRo7K77EzbvHmzSpYsmaPbfO211/Tyyy/bv4QUwJ0jyABwMH36dHXr1k3r16/X2bNnU8wfPny4unTponvuuUcrV67U4cOHNXfuXJUpU0YjRoxId92LFy+Wt7e36tate7fKz7AVK1aoTZs2ObrNli1b6vLly1q9enWObhf4NyPIALCLjY3VggULNHjwYDVu3FgzZ850mL9t2zZNmDBBkyZN0qRJk1S/fn2VKlVKNWrU0GuvvXbbX9Dz58+/bXjYsWOHihYtqrfffts+bdWqVapZs6bc3Nzk6+ur9u3b2+d9/vnnuv/+++Xl5SV/f391795d58+fv+2+rly5Uo888oikfy4BDRw4UIMHD1ahQoVUrFgxffrpp4qLi1Pfvn3l5eWlsmXLpti/zZs3q1atWnJ1dVXx4sX18ssv68aNG2luM2/evGrVqpXmz59/2/oAZAxBBoDdwoUL5e/vr1q1aqlHjx6aMWOGbv5e2Xnz5snT01PPPvtsqsvbbLZ0179lyxbdf//9ac7fsGGDmjVrpnHjxmn48OGSpK+++krt27dXq1attHv3bq1fv161atWyL5OQkKCxY8dq7969Wr58uU6cOKE+ffqkW8eBAwd0/vx5Pfjgg/Zps2bNkq+vr3766ScNHDhQzzzzjB599FE98MAD+vnnn9W8eXP17NlTV65ckSSdOXNGrVq1Us2aNbV3715NnTpV06dP15tvvpnutmvVqqXvv/8+3TYAMsHJ374NIBd54IEHzKhRo4wxxly+fNkUKFDAbNy40T7/oYceMqGhoQ7LTJw40Xh4eNiHS5cupbruixcvGknmu+++c5jeu3dv07ZtW7N06VLj6elp5s+f7zA/LCzM9OjRI8P7sGPHDiPJXL582RhjzMaNG40kc/HiRXubcePGmU6dOtnHGzZsaOrVq2cfv3HjhvHw8DA9e/a0Tzt37pyRZLZu3WqMMeaVV14xFSpUMElJSfY2H330kfH09DSJiYn29Q4aNMihvhUrVpg8efLY2wC4M/TIAJAkHT58WD/++KN69OghSfL09FTbtm01ffr0dJfr16+f9uzZo48//lhxcXEOPTg3u3r1qiTJzc0txbzt27fr0Ucf1eeff64uXbo4zNuzZ4+aNGmS5vZ37dqlNm3aqFSpUvLy8lLDhg0lSSdPnkxzmRUrVtgvKyULDQ21/ztv3rwqUqSIqlWrZp9WrFgxSbJftjp48KDCwsIceqHq1q2r2NhYnT59Os1tu7u7KykpSfHx8Wm2AZBxBBkAkv65ybdmzZoqV66cfVqPHj20ZMkSRUdHS5LKlSun3377TQkJCfY2BQsWVNmyZVWiRIl011+kSBHZbDZdvHgxxbyQkBBVrFhRM2bMcFi39M8v/rTExcWpRYsW8vb21pw5c7Rjxw4tW7ZMknT9+vVUlzl37px2796d4qms/PnzO4zbbDaHacmB5U6fOPr777/l4eGR7n4ByDiCDADduHFDs2fPVvfu3R2mN2/eXAUKFNC8efMkSd26dVNsbKymTJmS6W24uLiocuXK+vXXX1PM8/X11YYNG3Ts2DF17tzZIcyEhoZq/fr1qa7z0KFDunDhgsaPH6/69eurYsWKt73Rd9WqVXrggQdUuHDhTO/DzSpVqqStW7c69ED98MMP8vLyUmBgYJrL7d+/X/fee+8dbRvA/yPIANCXX36pqKgoVa1aVfv377cPhw8fVoMGDeyXl8LCwjRs2DANGzZMQ4cO1ZYtW/THH39o27Ztmj59umw2m/LkSfvHSosWLbRly5ZU5/n5+WnDhg06dOiQunXrZn/6Z9SoUZo3b55GjRqlgwcP6pdffrE/0VSqVCm5uLjoww8/1G+//aaVK1dq7Nix6e7rzU8r3Ylnn31Wp06d0sCBA3Xo0CGtWLFCo0aN0tChQ9M9Bt9//73DCwEB3CFn36QDwPkefvhhIyndYe/evfb2CxYsMI0aNTI+Pj4mf/78JjAw0HTv3t1s27Yt3e0cOHDAuLu7O9wQnHyzb7KzZ8+a8uXLm86dO5sbN24YY4xZsmSJueeee4yLi4vx9fU1HTp0sLefO3euKV26tHF1dTVhYWFm5cqVRpLZvXu3McbxZt/Y2Fjj5uZmjh496lBXajflBgUFmffee89hmiSzbNky+/imTZtMzZo1jYuLi/H39zfDhw83CQkJaa739OnTJn/+/ObUqVPpHicAGWczJo078wDgLnj00Ud133333fbleXfD0qVL9dprr6V6eSsnDB8+XBcvXtQnn3zilO0D/0ZcWgKQo9555x15eno6Zduenp4OL9rLaX5+fre99AUgc+iRAQAAlkWPDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsKz/A85ZbCCril5rAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "delta_G = protac_db['delta G (kcal/mol, Protac to E3)']\n",
        "delta_G = delta_G.dropna()\n",
        "delta_G = delta_G.astype(float)\n",
        "plt.hist(delta_G, bins=10)\n",
        "plt.xlabel('ΔG (kcal/mol)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title(f'Distribution of ΔG across PROTAC molecules')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X__g7xuJ2dfW"
      },
      "source": [
        "Let's take a closer look at the distribution of PROTAC molecules around the -10 range of ΔG values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "UfKjlNGY2kv5",
        "outputId": "93f2fda2-5d50-4b79-e5cc-6f67d96bf44c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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yAwYMcGhP6zbm7u7ucJpi4sSJRpIJCQlx2E4GDhyY4imNtEjtVF2XLl3MO++8Y+bNm2emTZtmnn/+eSPJtGzZMtVpJ/d5vHXrlgkODjYlSpQwN27csLcvXrzYSDJDhgxJ17Js2LDBNGvWzEyaNMksXLjQjBgxwuTKlct4eHiYrVu3pjp+UtvwiBEjjM1mM0ePHrW3Va1a1fj6+jq0GWNMfHy8/e/kPivbt283ksyLL77o0P7GG28YSWb16tXGGGMWLFiQ6v68V69exs/PL12n2CWZrl27mvPnz5tz586Z3377zdSqVctIMqNHj7YPFx4eburUqWPfR/z555+mQ4cOSZ7qy5EjhyldunSK83399deNJLNz585E/VI6VXf27FmTLVs28+WXX9rbKlWqZBo3buww3BNPPGH8/f1TXf6UJKy7bt262dtiY2NN3rx5jc1mMyNHjrS3X7p0yXh6ejp8P6Vln5/efdnXX39tJJkxY8Ykqj9hO0zuEoTDhw8n+lzenQWOHDlismbNaoYPH+4w7p9//mmyZctmb9+2bVuGnB53yVN1CXx8fFK8uy7hv7yFCxem+0Jqd3d3de7c2fLwHTt2dDh91Lx5c+XJk0c//vhjuuZv1Y8//qisWbPq9ddfd2jv16+fjDGJ7lCrXbu2Q4ovVaqU/Pz8Ur0b7ccff1RISIjatGljb8uePbtef/11Xbt2TevWrUtX/RcuXNDy5cvVokULZclye7Nr1aqVYmNj9d1339mHi4yMlHR73d/t0UcfVVBQkL1L+G8tNa+88kqitjv/e7x586b++ecfVahQQZK0devWVKdRpUoVXbhwwV5vwmnQ7t27OwyXcGFpAmOM5s2bp0aNGskYo3/++cfe1a1bV1euXLHP/8cff1SePHnUvHlz+/heXl7q1q2bpeVOzauvvurwOq3bWK1atRxOJ5cvX16S1KxZM4fPSEL7g7gTctKkSRo6dKiaNm2qDh06aOHChXrppZf03XffpftOtT/++EPnzp1T9+7d5eHhYW9v0KCBihYtqiVLlqRrupUqVdLcuXPVpUsXPf/88xowYID9aOfAgQNTHf/ObTgqKkr//POPKlWqJGOMtm3bJkk6f/681q9fry5duihfvnwO4yd1O/fd23nCfq1v374O7QlHmROWPWFfvHjxYsXExCRZb44cORQVFaUVK1akumxJmTRpkoKCghQcHKzy5ctrw4YN6tu3b6LHoPz000/2fUTJkiU1ffp0de7cWaNGjXIY7urVq0leCnCnhP4Jn3OrZs+erSxZsqhZs2b2tjZt2mjp0qW6dOmSvS0yMjLVGqy68waQrFmz6qmnnpIxRl27drW358iRQ0WKFHH4LKZ3n5+Wfdm8efMUGBiYaH8oJb0dptX8+fMVHx+vli1bOtQREhKiwoULa82aNZJkP6K0fPnyRJdapIVLB6dr166luFG1atVKzzzzjF588UXlzp1brVu31nfffZemEPXII4/Izc3N8vCFCxd2eG2z2VSoUKF7uobDiqNHjyo0NDTR+1GsWDF7/zvdvZOUpJw5czp8aJObT+HChe3hJrX5WPXtt98qJiZGrVq1src98cQTKly4sGbMmGFvS1i+pK5PWbhwoVasWKH//ve/aZp3gQIFErVdvHhRvXr1Uu7cueXp6amgoCD7cEmd6777/Uw47J/wfh49elRZsmRJNK+7TyOcP39ely9f1hdffOEQAoOCguwB/ty5c/ZpFipUKNGOpUiRIpaXPTnZsmVLdCr0XrexhJ1SWFhYku0pbXu3bt3SmTNnHLq4uLg0LFHyEr7kV65cma7xE5Y7qfe9aNGiKX4m4uLiEi3XrVu3kh2+UKFCaty4sdasWZPq8h87dkwREREKCAiQj4+PgoKC7Ne1JWzDCV+QJUqUSHkh/8/d22/Cdn33dhwSEqIcOXLYl71atWpq1qyZhg0bpsDAQDVu3FiTJ092uA6qe/fueuyxx1SvXj3lzZtXXbp0sXzdpSQ1btxYK1as0MqVK/Xbb7/pn3/+0ejRoxPtq8qXL68VK1Zo2bJl+u9//6scOXLo0qVLifbzvr6+qT72JqF/WsPNN998o6effloXLlzQgQMHdODAAT3xxBO6deuWw2lUPz+/DHv0TlKfRw8PD4c7/hLa7/wspnefn5Z92cGDB1WkSBFly3Z/rg7av3+/jDEqXLhwolr+/vtvex0FChRQ37599dVXXykwMFB169bV//73vzRd3yS58DVOJ06c0JUrV1I8f+3p6an169drzZo1WrJkiZYtW6Zvv/1WNWvW1E8//WTpVvW0nre2IrkEHRcX98Bun09uPuaui3wflBkzZihfvnz2ozoJWrVqpffff1+HDh3So48+qkKFCilbtmzatWtXomkkfCmk9cOX1Dpu2bKlfv31V7355psqU6aMfHx8FB8fr+eeey7J4J1R72fCtNu3b69OnTolOcydj+C4X9zd3RPtKNMqufckPe/Vr7/+qho1aji0HT58OEMeApsQ5C5evHjP00qr48ePJwoja9asSfLGkwRhYWG6deuWoqKi5Ofnl+QwcXFxevbZZ3Xx4kX1799fRYsWlbe3t06ePKmIiIh0H4FPbn+Y2lGBhIcDb9q0ST/88IOWL1+uLl26aPTo0dq0aZN8fHwUHBys7du3a/ny5Vq6dKmWLl2qyZMnq2PHjpYuRM6bN2+St/PfLTAw0D5c3bp1VbRoUTVs2FDjxo1zOHJWrFgxbdu2TdHR0XJ3d09yWjt37lT27NkT/cOckv3792vz5s2SEv+jLd3eFyYcNS5atKi2bdum48ePJ/qHI62S+tzdz++BjN6XpfS9aaWWhGcDJrXMd57BGD16tCIiIrRw4UL99NNPev311zVixAht2rTJ8nW1Lhucpk+fLun2hp+SLFmyqFatWqpVq5bGjBmjDz74QIMGDdKaNWtUu3btDH+66P79+x1eG2N04MABhw0kZ86cunz5cqJxjx49qkcffdT+Oi21hYeHa+XKlYkOL+/Zs8fePyOEh4dr586dio+Pd/hivZf5HD58WL/++qveeOONRMucEJxmzpypwYMHy9vbW9WrV9e6det08uRJPfLII/e2QEm4dOmSVq1apWHDhmnIkCH29rvXbVqEh4crPj5ehw8fdthZHjhwwGG4oKAg+fr6Ki4uLtUvgfDwcO3atUvGGIf3be/evemuM7X5PYhtLCmlS5dOdAonJCQkQ6adcNQltZsXkvs8Jiz33r17VbNmTYd+e/fuTfF9CQkJSbRcpUuXTrXehAe+JufPP//Uvn37NHXqVHXs2NHefve8EvY3Sf0jYkXCdr1//377EQjp9kXBly9fTrTsFSpUUIUKFTR8+HDNnDlT7dq10+zZs+2nkdzc3NSoUSM1atRI8fHx6t69uyZOnKi33377vj27qkGDBqpWrZo++OADvfzyy/L29pYkNWzYUBs3btScOXPUvn37ROMdOXJEP//8s2rXrp2mf7BnzJih7Nmza/r06Ym+xH/55Rd98sknOnbsmPLly6dGjRpp1qxZ+uabbyydnr0f0rvPT8u+rGDBgvrtt98UExOT7E0aCUfx7/7utHKWo2DBgjLGqECBAnrsscdSHb5kyZIqWbKkBg8erF9//VXPPPOMJkyYoPfffz/VcSUXPVW3evVqvffeeypQoID9NumkJPUfZMKD6xIOESd8SJIKMukxbdo0h0Orc+fO1enTpx0ewFawYEFt2rTJ4ZD84sWLE90WmZba6tevr7i4OH322WcO7R9//LFsNluyD5JMq/r16+vMmTP69ttv7W2xsbH69NNP5ePjYz/qkxYJp+KSunulRIkSKl68uMPpuiFDhiguLk7t27dP8pTdvf63lLAzu3s6Y8eOTfc0EwL++PHjHdo//fTTRPNu1qyZ5s2bl+SX2fnz5+1/169fX6dOnXL4mZ/r16/riy++SHedKXlQ21hScubMqdq1azt0d15PZEVkZGSiW+SNMfadYWr/hCX3eXzqqacUHBysCRMmOEx/6dKl+vvvv5O8czKBh4dHouVK+IK4c10n2LFjhxYtWqQ6deqkeEQwqW3YGJPo1v6goCBVrVpVX3/9tY4dO+bQz8rnKOHhkHd/NsaMGSNJ9mW/dOlSoundvS+++1EfWbJksf/DaeURG/eif//+unDhgr788kt728svv6zg4GC9+eabia6/u3nzpjp37ixjjMM/V1bMmDFDVapUUatWrdS8eXOH7s0335QkzZo1S9Lta2RLliyp4cOHJ/lg16tXrzo8NuJ+SO8+Py37smbNmumff/5JtG+R/v92GB4erqxZs2r9+vUO/e/epyaladOmypo1q4YNG5ZoOzTG2Le9yMjIRI/9KFmypLJkyZKmbdDpR5yWLl2qPXv2KDY2VmfPntXq1au1YsUKhYeHa9GiRSnuPN99912tX79eDRo0UHh4uM6dO6fx48crb9689mdiFCxYUDly5NCECRPk6+srb29vlS9fPsnrXqwICAhQ5cqV1blzZ509e1Zjx45VoUKFHB6Z8OKLL2ru3Ll67rnn1LJlSx08eFDffPNNolsu01Jbo0aNVKNGDQ0aNEhHjhxR6dKl9dNPP2nhwoXq3bt3htzOKUndunXTxIkTFRERoS1btih//vyaO3euNmzYoLFjx6brQsYZM2bI3d1dq1at0qpVqxL19/f318aNG7V161Y9+eSTqlKlij777DP17NlThQsXtj85/NatW9q3b59mzJghNze3dB+R8PPzU9WqVfXRRx8pJiZGjzzyiH766adUnweTkrJly6pZs2YaO3asLly4YH8cQcITf+88mjFy5EitWbNG5cuX10svvaTixYvr4sWL2rp1q1auXGn/h+Cll17SZ599po4dO2rLli3KkyePpk+fLi8vr3TXmZIHtY2lR0L42b17t6TbR6R/+eUXSbd/UUC6fVF/mzZt1KZNGxUqVMj+3LANGzaoW7duevLJJ1OcR0qfxw8//FCdO3dWtWrV1KZNG/vjCPLnz68+ffqka5latWolT09PVapUScHBwfrrr7/0xRdfyMvLSyNHjkxx3KJFi6pgwYJ64403dPLkSfn5+WnevHlJXkf2ySefqHLlynryySfVrVs3FShQQEeOHNGSJUtS/XmZ0qVLq1OnTvriiy90+fJlVatWTb///rumTp2qJk2a2E+vTp06VePHj9cLL7ygggUL6urVq/ryyy/l5+fn8GTuixcvqmbNmsqbN6+OHj2qTz/9VGXKlHE4mnU/1KtXTyVKlNCYMWPUo0cPZc+eXbly5dLcuXPVoEEDPfnkk4meHH7gwAGNGzcu2UfEJOW3336z39aflEceeURPPvmkZsyYof79+yt79uyaP3++ateurapVq6ply5Z65plnlD17du3evVszZ85Uzpw5U32W0724l32+1X1Zx44dNW3aNPXt21e///67qlSpoqioKK1cuVLdu3dX48aN5e/vrxYtWujTTz+VzWZTwYIFtXjxYvv1SSkpWLCg3n//fQ0cOFBHjhxRkyZN5Ovrq8OHD2vBggXq1q2b3njjDa1evVqvvfaaWrRooccee0yxsbH2I4N3Xsifqnu6J+8eJDyOIKFzc3MzISEh5tlnnzXjxo1L8onRd9+CuGrVKtO4cWMTGhpq3NzcTGhoqGnTpo3Zt2+fw3gLFy40xYsXN9myZXO4rTGlpwwn9ziCWbNmmYEDB5rg4GDj6elpGjRokOg2X2OMGT16tHnkkUeMu7u7eeaZZ8wff/yRaJop1Xb34wiMMebq1aumT58+JjQ01GTPnt0ULlzYjBo1yuG2YmOSfsquMck/JuFuZ8+eNZ07dzaBgYHGzc3NlCxZMslHJlh5HEHCbfpWuj59+jiMu23bNtOxY0eTL18+4+bmZry9vU2pUqVMv379Ej3pNykJ20tSj6s4ceKEeeGFF0yOHDmMv7+/adGihTl16lSi222Tm0bC9nvnrchRUVGmR48eJiAgwPj4+JgmTZqYvXv3GkkOtwQbc/s97tGjhwkLCzPZs2c3ISEhplatWuaLL75wGO7o0aPm+eefN15eXiYwMND06tXLLFu27J4fR+Dt7Z3kOPeyjSXcNjxq1ChLdaRFSttNgkOHDpkWLVqY/PnzGw8PD+Pl5WXKli1rJkyYkKj+5CT3eTTGmG+//dY88cQTxt3d3QQEBJh27dqZEydOpHuZxo0bZ55++mkTEBBgsmXLZvLkyWPat29v9u/fb2n8v/76y9SuXdv4+PiYwMBA89JLL9kf53H353XXrl327d3Dw8MUKVLEvP322/b+KX1WYmJizLBhw0yBAgVM9uzZTVhYmBk4cKC5efOmfZitW7eaNm3amHz58hl3d3cTHBxsGjZsaP744w/7MHPnzjV16tQxwcHBxs3NzeTLl8+8/PLL5vTp06kua3L7tLultE+aMmVKku/N4cOHzUsvvWTy5ctnsmfPbgIDA83zzz+f5JO875TU4wh69uxpJJmDBw8mO94777xjJJkdO3bY2y5dumSGDBliSpYsaby8vIyHh4cpUaKEGThwYKrvT3LrLrnPeVLfe1b3+XfvHxPGtbIvu379uhk0aJB9OwoJCTHNmzd3eK/Onz9vmjVrZry8vEzOnDnNyy+/bHbt2pXq4wgSzJs3z1SuXNl4e3sbb29vU7RoUdOjRw+zd+9eY8ztfUSXLl1MwYIFjYeHhwkICDA1atQwK1euTPK9TY7t/94MAPfB9u3b9cQTT+ibb75J8bQzAODfwSWvcQL+jZL6qYuxY8cqS5Ys9qf0AgD+3Zx+jRPwsPjoo4+0ZcsW1ahRQ9myZbPfct2tW7d7vtUYAOAaOFUHZJAVK1Zo2LBh+uuvv3Tt2jXly5dPHTp00KBBg+7bg98AAA8WwQkAAMAirnECAACwiOAEAABg0UN/4UV8fLxOnTolX1/fDP/5FQAAcH8YY3T16lWFhobe829rZqSHPjidOnWKO5oAAPiXOn78uOUf4H0QHvrglPC4+OPHjyf7S+MAAMC1REZGKiwsLF0/9XU/PfTBKeH0nJ+fH8EJAIB/GVe7zMapJw0///xzlSpVyh5qKlasqKVLl9r7V69eXTabzaF75ZVXnFgxAADIzJx6xClv3rwaOXKkChcuLGOMpk6dqsaNG2vbtm16/PHHJd3+hfh3333XPs79+mV4AACA1Dg1ODVq1Mjh9fDhw/X5559r06ZN9uDk5eWlkJAQZ5QHAADgwGXu74uLi9Ps2bMVFRWlihUr2ttnzJihwMBAlShRQgMHDtT169dTnE50dLQiIyMdOgAAgIzg9IvD//zzT1WsWFE3b96Uj4+PFixYoOLFi0uS2rZtq/DwcIWGhmrnzp3q37+/9u7dq/nz5yc7vREjRmjYsGEPqnwAAJCJOP236m7duqVjx47pypUrmjt3rr766iutW7fOHp7utHr1atWqVUsHDhxQwYIFk5xedHS0oqOj7a8Tbme8cuUKd9UBAPAvERkZKX9/f5f7/nZ6cLpb7dq1VbBgQU2cODFRv6ioKPn4+GjZsmWqW7eupem56hsPAACS56rf3y5zjVOC+Ph4hyNGd9q+fbskKU+ePA+wIgAAgNuceo3TwIEDVa9ePeXLl09Xr17VzJkztXbtWi1fvlwHDx7UzJkzVb9+feXKlUs7d+5Unz59VLVqVZUqVcqZZQMAgEzKqcHp3Llz6tixo06fPi1/f3+VKlVKy5cv17PPPqvjx49r5cqVGjt2rKKiohQWFqZmzZpp8ODBziwZAABkYi53jVNGc9VzpAAAIHmu+v3tctc4AQAAuCqCEwAAgEUEJwAAAIsITgAAABY5/SdXAMDV5R+wxNklZIgjIxs4uwTgX48jTgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwyKnB6fPPP1epUqXk5+cnPz8/VaxYUUuXLrX3v3nzpnr06KFcuXLJx8dHzZo109mzZ51YMQAAyMycGpzy5s2rkSNHasuWLfrjjz9Us2ZNNW7cWLt375Yk9enTRz/88IPmzJmjdevW6dSpU2ratKkzSwYAAJmYzRhjnF3EnQICAjRq1Cg1b95cQUFBmjlzppo3by5J2rNnj4oVK6aNGzeqQoUKlqYXGRkpf39/XblyRX5+fvezdAAPqfwDlji7hAxxZGQDZ5cAWOaq398uc41TXFycZs+eraioKFWsWFFbtmxRTEyMateubR+maNGiypcvnzZu3JjsdKKjoxUZGenQAQAAZASnB6c///xTPj4+cnd31yuvvKIFCxaoePHiOnPmjNzc3JQjRw6H4XPnzq0zZ84kO70RI0bI39/f3oWFhd3nJQAAAJmF04NTkSJFtH37dv3222969dVX1alTJ/3111/pnt7AgQN15coVe3f8+PEMrBYAAGRm2ZxdgJubmwoVKiRJKlu2rDZv3qxx48apVatWunXrli5fvuxw1Ons2bMKCQlJdnru7u5yd3e/32UDAIBMyOlHnO4WHx+v6OholS1bVtmzZ9eqVavs/fbu3atjx46pYsWKTqwQAABkVk494jRw4EDVq1dP+fLl09WrVzVz5kytXbtWy5cvl7+/v7p27aq+ffsqICBAfn5+6tmzpypWrGj5jjoAAICM5NTgdO7cOXXs2FGnT5+Wv7+/SpUqpeXLl+vZZ5+VJH388cfKkiWLmjVrpujoaNWtW1fjx493ZskAACATc7nnOGU0V30OBIB/D57jBDx4rvr97XLXOAEAALgqghMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKnBqcRI0aoXLly8vX1VXBwsJo0aaK9e/c6DFO9enXZbDaH7pVXXnFSxQAAIDNzanBat26devTooU2bNmnFihWKiYlRnTp1FBUV5TDcSy+9pNOnT9u7jz76yEkVAwCAzCybM2e+bNkyh9dTpkxRcHCwtmzZoqpVq9rbvby8FBIS8qDLAwAAcOBS1zhduXJFkhQQEODQPmPGDAUGBqpEiRIaOHCgrl+/nuw0oqOjFRkZ6dABAABkBKcecbpTfHy8evfurWeeeUYlSpSwt7dt21bh4eEKDQ3Vzp071b9/f+3du1fz589PcjojRozQsGHDHlTZAAAgE7EZY4yzi5CkV199VUuXLtUvv/yivHnzJjvc6tWrVatWLR04cEAFCxZM1D86OlrR0dH215GRkQoLC9OVK1fk5+d3X2oH8HDLP2CJs0vIEEdGNnB2CYBlkZGR8vf3d7nvb5c44vTaa69p8eLFWr9+fYqhSZLKly8vSckGJ3d3d7m7u9+XOgEAQObm1OBkjFHPnj21YMECrV27VgUKFEh1nO3bt0uS8uTJc5+rAwAAcOTU4NSjRw/NnDlTCxculK+vr86cOSNJ8vf3l6enpw4ePKiZM2eqfv36ypUrl3bu3Kk+ffqoatWqKlWqlDNLBwAAmZBTg9Pnn38u6fZDLu80efJkRUREyM3NTStXrtTYsWMVFRWlsLAwNWvWTIMHD3ZCtQAAILNz+qm6lISFhWndunUPqBoAAICUudRznAAAAFwZwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAonQFp0OHDmV0HQAAAC4vXcGpUKFCqlGjhr755hvdvHkzo2sCAABwSekKTlu3blWpUqXUt29fhYSE6OWXX9bvv/+e0bUBAAC4lHQFpzJlymjcuHE6deqUvv76a50+fVqVK1dWiRIlNGbMGJ0/fz6j6wQAAHC6e7o4PFu2bGratKnmzJmjDz/8UAcOHNAbb7yhsLAwdezYUadPn86oOgEAAJzunoLTH3/8oe7duytPnjwaM2aM3njjDR08eFArVqzQqVOn1Lhx44yqEwAAwOnSFZzGjBmjkiVLqlKlSjp16pSmTZumo0eP6v3331eBAgVUpUoVTZkyRVu3bk1xOiNGjFC5cuXk6+ur4OBgNWnSRHv37nUY5ubNm+rRo4dy5colHx8fNWvWTGfPnk1P2QAAAPckXcHp888/V9u2bXX06FF9//33atiwobJkcZxUcHCwJk2alOJ01q1bpx49emjTpk1asWKFYmJiVKdOHUVFRdmH6dOnj3744QfNmTNH69at06lTp9S0adP0lA0AAHBPbMYY4+wiEpw/f17BwcFat26dqlatqitXrigoKEgzZ85U8+bNJUl79uxRsWLFtHHjRlWoUCHVaUZGRsrf319XrlyRn5/f/V4EAA+h/AOWOLuEDHFkZANnlwBY5qrf3+k64jR58mTNmTMnUfucOXM0derUdBdz5coVSVJAQIAkacuWLYqJiVHt2rXtwxQtWlT58uXTxo0b0z0fAACA9EhXcBoxYoQCAwMTtQcHB+uDDz5IVyHx8fHq3bu3nnnmGZUoUUKSdObMGbm5uSlHjhwOw+bOnVtnzpxJcjrR0dGKjIx06AAAADJCuoLTsWPHVKBAgUTt4eHhOnbsWLoK6dGjh3bt2qXZs2ena/wEI0aMkL+/v70LCwu7p+kBAAAkSFdwCg4O1s6dOxO179ixQ7ly5Urz9F577TUtXrxYa9asUd68ee3tISEhunXrli5fvuww/NmzZxUSEpLktAYOHKgrV67Yu+PHj6e5HgAAgKSkKzi1adNGr7/+utasWaO4uDjFxcVp9erV6tWrl1q3bm15OsYYvfbaa1qwYIFWr16d6ChW2bJllT17dq1atcretnfvXh07dkwVK1ZMcpru7u7y8/Nz6AAAADJCtvSM9N577+nIkSOqVauWsmW7PYn4+Hh17NgxTdc49ejRQzNnztTChQvl6+trv27J399fnp6e8vf3V9euXdW3b18FBATIz89PPXv2VMWKFS3dUQcAAJCR7ulxBPv27dOOHTvk6empkiVLKjw8PG0zt9mSbJ88ebIiIiIk3X4AZr9+/TRr1ixFR0erbt26Gj9+fLKn6u7mqrczAvj34HEEwIPnqt/fLvUcp/vBVd94AP8eBCfgwXPV7+90naqLi4vTlClTtGrVKp07d07x8fEO/VevXp0hxQEAALiSdAWnXr16acqUKWrQoIFKlCiR7Ck3AACAh0m6gtPs2bP13XffqX79+hldDwAAgMtK1+MI3NzcVKhQoYyuBQAAwKWlKzj169dP48aN00N+XTkAAICDdJ2q++WXX7RmzRotXbpUjz/+uLJnz+7Qf/78+RlSHAAAgCtJV3DKkSOHXnjhhYyuBQAAwKWlKzhNnjw5o+sAAABweem6xkmSYmNjtXLlSk2cOFFXr16VJJ06dUrXrl3LsOIAAABcSbqOOB09elTPPfecjh07pujoaD377LPy9fXVhx9+qOjoaE2YMCGj6wQAAHC6dB1x6tWrl5566ildunRJnp6e9vYXXnhBq1atyrDiAAAAXEm6jjj9/PPP+vXXX+Xm5ubQnj9/fp08eTJDCgMAAHA16TriFB8fr7i4uETtJ06ckK+v7z0XBQAA4IrSFZzq1KmjsWPH2l/bbDZdu3ZNQ4cO5WdYAADAQytdp+pGjx6tunXrqnjx4rp586batm2r/fv3KzAwULNmzcroGgEAAFxCuoJT3rx5tWPHDs2ePVs7d+7UtWvX1LVrV7Vr187hYnEAAICHSbqCkyRly5ZN7du3z8haAAAAXFq6gtO0adNS7N+xY8d0FQMAAODK0hWcevXq5fA6JiZG169fl5ubm7y8vAhOAADgoZSuu+ouXbrk0F27dk179+5V5cqVuTgcAAA8tNL9W3V3K1y4sEaOHJnoaBQAAMDDIsOCk3T7gvFTp05l5CQBAABcRrqucVq0aJHDa2OMTp8+rc8++0zPPPNMhhQGAADgatIVnJo0aeLw2mazKSgoSDVr1tTo0aMzoi4AAACXk67gFB8fn9F1AAAAuLwMvcYJAADgYZauI059+/a1POyYMWPSMwsAAACXk67gtG3bNm3btk0xMTEqUqSIJGnfvn3KmjWrnnzySftwNpstY6oEAABwAekKTo0aNZKvr6+mTp2qnDlzSrr9UMzOnTurSpUq6tevX4YWCQAA4ArSdY3T6NGjNWLECHtokqScOXPq/fff5646AADw0EpXcIqMjNT58+cTtZ8/f15Xr16956IAAABcUbqC0wsvvKDOnTtr/vz5OnHihE6cOKF58+apa9euatq0aUbXCAAA4BLSdY3ThAkT9MYbb6ht27aKiYm5PaFs2dS1a1eNGjUqQwsEAABwFekKTl5eXho/frxGjRqlgwcPSpIKFiwob2/vDC0OAADAldzTAzBPnz6t06dPq3DhwvL29pYxJqPqAgAAcDnpCk4XLlxQrVq19Nhjj6l+/fo6ffq0JKlr1648igAAADy00hWc+vTpo+zZs+vYsWPy8vKyt7dq1UrLli3LsOIAAABcSbqucfrpp5+0fPly5c2b16G9cOHCOnr0aIYUBgAA4GrSdcQpKirK4UhTgosXL8rd3f2eiwIAAHBF6QpOVapU0bRp0+yvbTab4uPj9dFHH6lGjRoZVhwAAIArSdepuo8++ki1atXSH3/8oVu3buk///mPdu/erYsXL2rDhg0ZXSMAAIBLSNcRpxIlSmjfvn2qXLmyGjdurKioKDVt2lTbtm1TwYIFLU9n/fr1atSokUJDQ2Wz2fT999879I+IiJDNZnPonnvuufSUDAAAcM/SfMQpJiZGzz33nCZMmKBBgwbd08yjoqJUunRpdenSJdmfannuuec0efJk+2uuoQIAAM6S5uCUPXt27dy5M0NmXq9ePdWrVy/FYdzd3RUSEpIh8wMAALgX6TpV1759e02aNCmja0nS2rVrFRwcrCJFiujVV1/VhQsXUhw+OjpakZGRDh0AAEBGSNfF4bGxsfr666+1cuVKlS1bNtFv1I0ZMyZDinvuuefUtGlTFShQQAcPHtRbb72levXqaePGjcqaNWuS44wYMULDhg3LkPkDAADcKU3B6dChQ8qfP7927dqlJ598UpK0b98+h2FsNluGFde6dWv73yVLllSpUqVUsGBBrV27VrVq1UpynIEDB6pv377215GRkQoLC8uwmgAAQOaVpuBUuHBhnT59WmvWrJF0+ydWPvnkE+XOnfu+FHe3Rx99VIGBgTpw4ECywcnd3Z0LyAEAwH2RpmucjDEOr5cuXaqoqKgMLSglJ06c0IULF5QnT54HNk8AAIAE6brGKcHdQSqtrl27pgMHDthfHz58WNu3b1dAQIACAgI0bNgwNWvWTCEhITp48KD+85//qFChQqpbt+49zRcAACA90hScEh5CeXdbev3xxx8OP9GScG1Sp06d9Pnnn2vnzp2aOnWqLl++rNDQUNWpU0fvvfcep+IAAIBTpCk4GWMUERFhDy43b97UK6+8kuiuuvnz51uaXvXq1VM8arV8+fK0lAcAAHBfpSk4derUyeF1+/btM7QYAAAAV5am4HTnT58AAABkNul6cjgAAEBmRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABY5NTitX79ejRo1UmhoqGw2m77//nuH/sYYDRkyRHny5JGnp6dq166t/fv3O6dYAACQ6Tk1OEVFRal06dL63//+l2T/jz76SJ988okmTJig3377Td7e3qpbt65u3rz5gCsFAACQsjlz5vXq1VO9evWS7GeM0dixYzV48GA1btxYkjRt2jTlzp1b33//vVq3bv0gSwUAAHDda5wOHz6sM2fOqHbt2vY2f39/lS9fXhs3bkx2vOjoaEVGRjp0AAAAGcFlg9OZM2ckSblz53Zoz507t71fUkaMGCF/f397FxYWdl/rBAAAmYfLBqf0GjhwoK5cuWLvjh8/7uySAADAQ8Jlg1NISIgk6ezZsw7tZ8+etfdLiru7u/z8/Bw6AACAjOCywalAgQIKCQnRqlWr7G2RkZH67bffVLFiRSdWBgAAMiun3lV37do1HThwwP768OHD2r59uwICApQvXz717t1b77//vgoXLqwCBQro7bffVmhoqJo0aeK8ogEAQKbl1OD0xx9/qEaNGvbXffv2lSR16tRJU6ZM0X/+8x9FRUWpW7duunz5sipXrqxly5bJw8PDWSUDAIBMzGaMMc4u4n6KjIyUv7+/rly5wvVOANIl/4Alzi4hQxwZ2cDZJQCWuer3t8te4wQAAOBqCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAItcOji98847stlsDl3RokWdXRYAAMiksjm7gNQ8/vjjWrlypf11tmwuXzIAAHhIuXwKyZYtm0JCQpxdBgAAgGufqpOk/fv3KzQ0VI8++qjatWunY8eOpTh8dHS0IiMjHToAAICM4NLBqXz58poyZYqWLVumzz//XIcPH1aVKlV09erVZMcZMWKE/P397V1YWNgDrBgAADzMbMYY4+wirLp8+bLCw8M1ZswYde3aNclhoqOjFR0dbX8dGRmpsLAwXblyRX5+fg+qVAAPkfwDlji7hAxxZGQDZ5cAWBYZGSl/f3+X+/52+Wuc7pQjRw499thjOnDgQLLDuLu7y93d/QFWBQAAMguXPlV3t2vXrungwYPKkyePs0sBAACZkEsHpzfeeEPr1q3TkSNH9Ouvv+qFF15Q1qxZ1aZNG2eXBgAAMiGXPlV34sQJtWnTRhcuXFBQUJAqV66sTZs2KSgoyNmlAQCATMilg9Ps2bOdXQIAAICdS5+qAwAAcCUEJwAAAIsITgAAABa59DVOAICMw4M8gXvHEScAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFiUzdkFAHh45R+wxNklAECG4ogTAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGDRvyI4/e9//1P+/Pnl4eGh8uXL6/fff3d2SQAAIBNy+eD07bffqm/fvho6dKi2bt2q0qVLq27dujp37pyzSwMAAJmMywenMWPG6KWXXlLnzp1VvHhxTZgwQV5eXvr666+dXRoAAMhkXDo43bp1S1u2bFHt2rXtbVmyZFHt2rW1ceNGJ1YGAAAyo2zOLiAl//zzj+Li4pQ7d26H9ty5c2vPnj1JjhMdHa3o6Gj76ytXrkiSIiMj71+hAJIUH33d2SXgIcT+PHNIWM/GGCdX4silg1N6jBgxQsOGDUvUHhYW5oRqAAAZzX+ssyvAg3T16lX5+/s7uww7lw5OgYGBypo1q86ePevQfvbsWYWEhCQ5zsCBA9W3b1/76/j4eF28eFG5cuWSzWa7r/X+W0VGRiosLEzHjx+Xn5+fs8vJ9FgfroX14VpYH67lfq4PY4yuXr2q0NDQDJ3uvXLp4OTm5qayZctq1apVatKkiaTbQWjVqlV67bXXkhzH3d1d7u7uDm05cuS4z5U+HPz8/NgRuRDWh2thfbgW1odruV/rw5WONCVw6eAkSX379lWnTp301FNP6emnn9bYsWMVFRWlzp07O7s0AACQybh8cGrVqpXOnz+vIUOG6MyZMypTpoyWLVuW6IJxAACA+83lg5Mkvfbaa8memsO9c3d319ChQxOd4oRzsD5cC+vDtbA+XEtmXB8242r3+QEAALgol34AJgAAgCshOAEAAFhEcAIAALCI4AQAAGARwSkTGz58uCpVqiQvL69UHxJ64cIF5c2bVzabTZcvX34g9WVGqa2THTt2qE2bNgoLC5Onp6eKFSumcePGPfhCMwkrn5Fjx46pQYMG8vLyUnBwsN58803FxsY+2EIzqX379qlx48YKDAyUn5+fKleurDVr1ji7rExvyZIlKl++vDw9PZUzZ077A6wfFgSnTOzWrVtq0aKFXn311VSH7dq1q0qVKvUAqsrcUlsnW7ZsUXBwsL755hvt3r1bgwYN0sCBA/XZZ5894Eozh9TWR1xcnBo0aKBbt27p119/1dSpUzVlyhQNGTLkAVeaOTVs2FCxsbFavXq1tmzZotKlS6thw4Y6c+aMs0vLtObNm6cOHTqoc+fO2rFjhzZs2KC2bds6u6yMZZDpTZ482fj7+yfbf/z48aZatWpm1apVRpK5dOnSA6sts0ptndype/fupkaNGve3oEwuufXx448/mixZspgzZ87Y2z7//HPj5+dnoqOjH2CFmc/58+eNJLN+/Xp7W2RkpJFkVqxY4cTKMq+YmBjzyCOPmK+++srZpdxXHHFCiv766y+9++67mjZtmrJkYXNxRVeuXFFAQICzy8iUNm7cqJIlSzr8kkHdunUVGRmp3bt3O7Gyh1+uXLlUpEgRTZs2TVFRUYqNjdXEiRMVHByssmXLOru8TGnr1q06efKksmTJoieeeEJ58uRRvXr1tGvXLmeXlqH4JkSyoqOj1aZNG40aNUr58uVzdjlIwq+//qpvv/1W3bp1c3YpmdKZM2cS/fxTwmtOF91fNptNK1eu1LZt2+Tr6ysPDw+NGTNGy5YtU86cOZ1dXqZ06NAhSdI777yjwYMHa/HixcqZM6eqV6+uixcvOrm6jENwesgMGDBANpstxW7Pnj2WpjVw4EAVK1ZM7du3v89VP9wycp3cadeuXWrcuLGGDh2qOnXq3IfKH073a30gY1hdP8YY9ejRQ8HBwfr555/1+++/q0mTJmrUqJFOnz7t7MV4qFhdJ/Hx8ZKkQYMGqVmzZipbtqwmT54sm82mOXPmOHkpMs6/4rfqYF2/fv0UERGR4jCPPvqopWmtXr1af/75p+bOnStJMv/36zyBgYEaNGiQhg0bdk+1ZhYZuU4S/PXXX6pVq5a6deumwYMH30N1mU9Gro+QkBD9/vvvDm1nz56190PaWV0/q1ev1uLFi3Xp0iX5+flJksaPH68VK1Zo6tSpGjBgwAOoNnOwuk4SAmvx4sXt7e7u7nr00Ud17Nix+1niA0VwesgEBQUpKCgoQ6Y1b9483bhxw/568+bN6tKli37++WcVLFgwQ+aRGWTkOpGk3bt3q2bNmurUqZOGDx+eYdPNLDJyfVSsWFHDhw/XuXPnFBwcLElasWKF/Pz8HL48YJ3V9XP9+nVJSnTtZZYsWexHPpAxrK6TsmXLyt3dXXv37lXlypUlSTExMTpy5IjCw8Pvd5kPDMEpEzt27JguXryoY8eOKS4uTtu3b5ckFSpUSD4+PonC0T///CNJKlasWKrPfUL6pLZOdu3apZo1a6pu3brq27ev/TqarFmzZmg4w22prY86deqoePHi6tChgz766COdOXNGgwcPVo8ePTLVr8U7Q8WKFZUzZ0516tRJQ4YMkaenp7788ksdPnxYDRo0cHZ5mZKfn59eeeUVDR06VGFhYQoPD9eoUaMkSS1atHBydRnI2bf1wXk6depkJCXq1qxZk+Twa9as4XEE91lq62To0KFJ9g8PD3dq3Q8rK5+RI0eOmHr16hlPT08TGBho+vXrZ2JiYpxXdCayefNmU6dOHRMQEGB8fX1NhQoVzI8//ujssjK1W7dumX79+png4GDj6+trateubXbt2uXssjKUzZj/u3AFAAAAKeKuOgAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAD9SkSZNUp04d++uIiAg1adLkvs1v7dq1stlsunz58n2bR3KqV6+u3r17219XqFBB8+bNe+B1AMg4BCcADjZu3CibzZbi733NmzdPNWvWVM6cOeXp6akiRYqoS5cu2rZtW4rTvnnzpt5++20NHTo0o8tOs3Xr1iksLOyBznPw4MEaMGAAP0IL/IsRnAA4mDRpktq0aaNVq1bp1KlTifr3799frVq1UpkyZbRo0SLt3btXM2fO1KOPPqqBAwemOO25c+fKz89PzzzzzP0q37KFCxeqUaNGD3Se9erV09WrV7V06dIHOl8AGYfgBMDu2rVr+vbbb9W7d2/VqFFDU6ZMcei/adMmffTRRxozZozGjBmjKlWqKF++fCpbtqwGDx6caiCYPXt2qmFl8+bNCgoK0ocffmhv++GHH1SuXDl5eHgoMDBQL7zwgr3f9OnT9dRTT8nX11chISFq27atzp07l+qyLlq0SM8//7yk26fUevbsqd69eytnzpzKnTu3vvzyS0VFRalz587y9fVVoUKFEi3funXr9PTTT8vd3V158uTRgAEDFBsbm+w8s2bNqvr162v27Nmp1gfANRGcANh99913CgkJ0dNPP6127drp66+/1p2/Az5r1iz5+Pioe/fuSY5vs9lSnP4vv/yip556Ktn+q1ev1rPPPqvhw4erf//+kqQlS5bohRdeUP369bVt2zatWrVKTz/9tH2cmJgYvffee9qxY4e+//57HTlyRBERESnWsXv3bp07d041a9a0t02dOlWBgYH6/fff1bNnT7366qtq0aKFKlWqpK1bt6pOnTrq0KGDrl+/Lkk6efKk6tevr3LlymnHjh36/PPPNWnSJL3//vspzvvpp5/Wzz//nOIwAFyYAYD/U6lSJTN06FBjjDFXr141Xl5eZs2aNfb+zz33nClVqpTDOKNHjzbe3t727vLly0lO+9KlS0aSWb9+vUN7p06dTOPGjc38+fONj4+PmT17tkP/ihUrmnbt2llehs2bNxtJ5urVq8YYY9asWWMkmUuXLtmHGT58uGnevLn9dbVq1UzlypXtr2NjY423t7fp0KGDve306dNGktm4caMxxpi33nrLFClSxMTHx9uH+d///md8fHxMXFycfbq9evVyqG/hwoUmS5Ys9mEA/LtwxAmAJGnv3r369ddf1a5dO0mSj4+PGjdurEmTJqU4XpcuXbR9+3ZNnDhRUVFRDkeo7nTjxg1JkoeHR6J+v/32m1q0aKHp06erVatWDv22b9+uWrVqJTv/LVu2qFGjRsqXL598fX1VrVo1SdKxY8eSHWfhwoX203QJSpUqZf87a9asypUrl0qWLGlvy507tyTZTwP+/fffqlixosNRtmeeeUbXrl3TiRMnkp23p6en4uPjFR0dnewwAFwXwQmApNsXhZcrV06FCxe2t7Vr107z5s3TlStXJEmFCxfWoUOHFBMTYx8mR44cKlSokB555JEUp58rVy7ZbDZdunQpUb+CBQuqaNGi+vrrrx2mLd0OGsmJiopS3bp15efnpxkzZmjz5s1asGCBJOnWrVtJjnP69Glt27Yt0V2D2bNnd3hts9kc2hIC0r3eEXfx4kV5e3unuFwAXBfBCYBiY2M1bdo0tW3b1qG9Tp068vLy0qxZsyRJbdq00bVr1zR+/Pg0z8PNzU3FixfXX3/9lahfYGCgVq9erQMHDqhly5YO4alUqVJatWpVktPcs2ePLly4oJEjR6pKlSoqWrRoqheG//DDD6pUqZICAgLSvAx3KlasmDZu3OhwhG3Dhg3y9fVV3rx5kx1v165deuKJJ+5p3gCch+AEQIsXL9bZs2dVokQJ7dq1y97t3btXVatWtZ+uq1ixovr166d+/fqpb9+++uWXX3T06FFt2rRJkyZNks1mU5Ysye9W6tatq19++SXJfsHBwVq9erX27NmjNm3a2O9OGzp0qGbNmqWhQ4fq77//1p9//mm/4y5fvnxyc3PTp59+qkOHDmnRokV67733UlzWO++muxfdu3fX8ePH1bNnT+3Zs0cLFy7U0KFD1bdv3xTfg59//tnhAaAA/mWcfZEVAOdr2LChkZRit2PHDvvw3377ralevbrx9/c32bNnN3nz5jVt27Y1mzZtSnE+u3fvNp6eng4XkCdcHJ7g1KlT5rHHHjMtW7Y0sbGxxhhj5s2bZ8qUKWPc3NxMYGCgadq0qX34mTNnmvz58xt3d3dTsWJFs2jRIiPJbNu2zRjjeHH4tWvXjIeHh9m/f79DXUldxB0eHm4+/vhjhzZJZsGCBfbXa9euNeXKlTNubm4mJCTE9O/f38TExCQ73RMnTpjs2bOb48ePp/g+AXBdNmOSuZITAO6DFi1a6Mknn0z1YZn3w/z58zV48OAkTxc+CP3799elS5f0xRdfOGX+AO4dp+oAPFCjRo2Sj4+PU+bt4+Pj8GDNBy04ODjVU4kAXBtHnAAAACziiBMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARf8PyWiheb55KJ4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "delta_G = protac_db['delta G (kcal/mol, Protac to E3)']\n",
        "delta_G = delta_G.dropna()\n",
        "delta_G = delta_G.astype(float)\n",
        "\n",
        "x_min = -15\n",
        "x_max = -5\n",
        "bin_size = 1\n",
        "bins = np.arange(x_min, x_max, bin_size)\n",
        "\n",
        "plt.hist(delta_G, bins=bins)\n",
        "plt.xlabel('ΔG (kcal/mol)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of ΔG ranged from -15 to -5 across PROTAC molecules')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLgrcWwxrO8k"
      },
      "source": [
        "There does not appear to be a lot of information on the spontaneity of PROTAC reactions but it is worth noting that the ones with recorded ΔGs appear energetically favorable, as expected.\n",
        "\n",
        "Let's now take a look at the $K_d$ values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "0ingXOVd_H0-",
        "outputId": "53a120a0-27f9-43d5-d7b3-9ef09969feff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "kd_data = protac_db['Kd (nM, Ternary complex)']\n",
        "kd_data = kd_data.dropna()\n",
        "kd_data = kd_data[~kd_data.str.contains('/')]\n",
        "kd_data = kd_data.astype(float)\n",
        "plt.hist(kd_data)\n",
        "plt.xlabel('Dissociation constant (nM)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Kd values across PROTAC molecules')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53a_41zHsyzh"
      },
      "source": [
        "Similar to ΔG values, there does not appear to be a lot of information on the affinity of formed PROTAC complexes. Since the range is so large, let's plot a second histogram focused on the PROTACs with low $K_d$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "07E77uos0DHk",
        "outputId": "ecd37abb-9da5-4592-a561-47212fcd05e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "kd_data = protac_db['Kd (nM, Ternary complex)']\n",
        "kd_data = kd_data.dropna()\n",
        "kd_data = kd_data[~kd_data.str.contains('/')]\n",
        "kd_data = kd_data.astype(float)\n",
        "\n",
        "# limit range\n",
        "x_max = 1500\n",
        "x_min = 0\n",
        "bin_size = 25\n",
        "bins = np.arange(x_min, x_max, bin_size)\n",
        "\n",
        "plt.hist(kd_data, bins=bins)\n",
        "\n",
        "plt.xlabel('Dissociation constant (nM)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Kd values ranged from 0-1500 across PROTAC molecules')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_An-9Yyf13Wf"
      },
      "source": [
        "The improved resolution of values illustrates a much cleaner distribution of $K_d$ values. We do see that a number of them have low, favorable $K_d$ values indicating that the PROTAC linker can form a strong connection with the E3 ligase and target protein."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07W0ZR08sdmn"
      },
      "source": [
        "Let's now take a look at XLogP3 values. Note that this is slightly different than the typical LogP partition coefficient. Recall that LogP is defined $$LogP = log(\\frac{[solute]_{oct}}{[solute]_{H_2O}})$$\n",
        "\n",
        "In other words, LogP is the measured ratio of the concentration of a compound in the organic phase to the its concentration in the aqueous phase, measuring the compound's solubility. XLogP3 is a knowledge-based method for calculating the partition coefficient by accounting for the molecular structure, presence of functional groups, and bonding [[8]](#8). Both properties estimate a compound's liphophilicity, giving insight into how a compound may behave in biological systems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "uLSXiosgBaQr",
        "outputId": "0fc9d1a1-c224-4f31-a1f2-70cef6892bc4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(protac_db['XLogP3'])\n",
        "plt.xlabel('XLogP3 Values')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of XLogP3 values across PROTAC molecules')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Pi3J8LFtdfw"
      },
      "source": [
        "All PROTAC compounds have a recorded XLogP3 value. The distribution looks normally distributed with few molecules with extreme logP profiles.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZfXNwTbCiO7"
      },
      "source": [
        "Now, let's take a look at the PROTAC degradation properties. \"DC50 (nM)\" and \"Dmax (%)\"  represent the half maximal degradation concentration and maximal degradation of the target protein of interest, respectively. Let's take a quick look at their distributions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KnlvRC0RvuWj"
      },
      "source": [
        "Let's first do some data cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Nqy8Gz2TkKJi"
      },
      "outputs": [],
      "source": [
        "# Let's first drop all the NaN values\n",
        "raw_dc50 = protac_db['DC50 (nM)']\n",
        "raw_dc50 = raw_dc50.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dux-7AKyvzbF"
      },
      "source": [
        "Notice that the values are all in string format with non-numerical characters such as '<', '/', and '>'. For the time being, let's remove these values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "6ivb0CpjvzED"
      },
      "outputs": [],
      "source": [
        "raw_dc50 = raw_dc50[~raw_dc50.str.contains('<|>|/|~|-')]\n",
        "raw_dc50 = raw_dc50.astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "5v30RIBAvbw9",
        "outputId": "60f3ab69-f072-44fd-d36e-3e56312259e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(raw_dc50.values, bins=75)\n",
        "plt.xlabel('PROTACs')\n",
        "plt.ylabel('DC50 (nM)')\n",
        "plt.title('DC50 for all PROTACs')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JON2ICSyaYe"
      },
      "source": [
        "The distribution is certainly skewed and has a few outliers. Let's log normalize."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "7ZwuZjSLvWuJ"
      },
      "outputs": [],
      "source": [
        "lognorm_dc50 = np.log(raw_dc50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "zP3NeH01yw3X",
        "outputId": "bb95e305-d94d-41f0-acd9-39adcc69a91b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(lognorm_dc50, bins=15)\n",
        "plt.xlabel('Log normalized DC50 values (log nM)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of log normalized DC50 values')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tk9niYXoVvNg"
      },
      "source": [
        "Now, let's take a look at Dmax percentage which represents the maximal degradation a PROTAC can elicit relative to the total activity of the target protein of interest [[7]](#7)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "3LlETiauDiR0"
      },
      "outputs": [],
      "source": [
        "# Using the same row indices as our cleaned DC50 data\n",
        "dmax = protac_db.iloc[lognorm_dc50.index]['Dmax (%)']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ShU6zSMNzo51"
      },
      "outputs": [],
      "source": [
        "# Following the same data cleaning procedure:\n",
        "dmax = dmax.dropna()\n",
        "dmax = dmax[~dmax.str.contains('<|>|/|~|-')]\n",
        "dmax = dmax.astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "pPdwTZ-l0VHS",
        "outputId": "726b2a80-f6da-46cd-b29a-1a76b45dc054"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(dmax.values, bins=10)\n",
        "plt.xlabel('Dmax (%)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Dmax (%)')\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mz7g6hvQ01nq"
      },
      "source": [
        "Notice that Dmax is represented as a percentage. For now, let's continue with regressing on DC50. We are now ready to featurize!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dpPN2j-0dd0",
        "outputId": "df1e32cb-9eac-4f78-8cd0-ee973613dc4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 599 PROTAC samples.\n"
          ]
        }
      ],
      "source": [
        "# Let's predict DC50 properties for the time being\n",
        "cleaned_data = protac_db.iloc[lognorm_dc50.index]\n",
        "print('There are {} PROTAC samples.'.format(cleaned_data.shape[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jBuHIBJMBgBb"
      },
      "outputs": [],
      "source": [
        "protac_smiles = cleaned_data['Smiles']\n",
        "dc_vals = lognorm_dc50"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mq3y74gZP182"
      },
      "source": [
        "## 3. Featurization\n",
        "\n",
        "Let's featurize using CircularFingerprint which is incorporated in DeepChem! CircularFingerprint is a common featurizer for molecules that encode local information about each atom and their neighborhood. For more information, the reader is refered to [[9]](#9)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ZLHNs-zlDGVU"
      },
      "outputs": [],
      "source": [
        "from rdkit import Chem\n",
        "featurizer = dc.feat.CircularFingerprint(radius=4, chiral=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JMeqGXB9E2tR"
      },
      "outputs": [],
      "source": [
        "features = featurizer.featurize(protac_smiles)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "u_Dam_xS6ZaN"
      },
      "outputs": [],
      "source": [
        "# Let's initialize our dataset and perform splits\n",
        "dataset = dc.data.NumpyDataset(X = features, y = dc_vals, ids=list(protac_smiles))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "xg4P05_E7TTJ"
      },
      "outputs": [],
      "source": [
        "splitter = dc.splits.RandomSplitter()\n",
        "train_random, val_random, test_random = splitter.train_valid_test_split(dataset, seed=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZqupuwnO3Xf"
      },
      "source": [
        "Along with a random split, let's also use a scaffold split which ensures that the split contain a structurally diverse array of compounds. Scaffold split groups molecules according to presence of rings, linkers, combinations of rings and linkers, as well as atomic properties. In general, scaffold splits are a good way of ensuring generalizability of our models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ltA3KGy5K4f2"
      },
      "outputs": [],
      "source": [
        "# Scaffold split\n",
        "splitter = dc.splits.ScaffoldSplitter()\n",
        "train_scaffold, val_scaffold, test_scaffold = splitter.train_valid_test_split(dataset, seed=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nOkE5R1dRrUS"
      },
      "source": [
        "To see the scaffold split in action, let's visualize the chosen compounds across the splits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "4d6ZMpU-WnA-",
        "outputId": "495372f6-ead4-4ea8-f22c-35493646f033"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Three molecules from the train set:\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Three molecules from the train set:\")\n",
        "np.random.seed(42)\n",
        "indices = np.random.choice(len(train_scaffold), size=3, replace=False)\n",
        "\n",
        "smiles = [train_scaffold.ids[index] for index in indices]\n",
        "mols = [Chem.MolFromSmiles(smile) for smile in smiles]\n",
        "\n",
        "Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(450, 350))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "CackYq8fZBG-",
        "outputId": "c25fea90-8e87-427c-ec24-1536b3af3175"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Three molecules from the validation set:\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Three molecules from the validation set:\")\n",
        "indices = np.random.choice(len(val_scaffold), size=3, replace=False)\n",
        "\n",
        "smiles = [val_scaffold.ids[index] for index in indices]\n",
        "mols = [Chem.MolFromSmiles(smile) for smile in smiles]\n",
        "\n",
        "Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(450, 350))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "OfYbU87cZula",
        "outputId": "d37b9f0d-781d-43d7-a9ab-1c36ef00cedc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Five molecules from the test set:\n"
          ]
        },
        {
          "data": {
            "image/png": 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CgV69sHQprKxkT4+oNLBWKAa/+qqpsyAiKkNycnJ27dolgLN37445fHjq1KkzTZ0SPV+DBg3Cw8OlhbSRLOlfRlqtNj4+/sKFC2fOnDlz5szp06fv3LljY2PTQNp2YW4iIiLOnj2rVCo7duxYtWrVJ5/g6OiofGx5Zl5e3sqVK+/evbt8+fJhUicCMk5ZKunnzsWKFRACgwZh9WrZw48fPz40NFSn0w0dOhSAi4tL69atfX19W7Vq1bBhQ8WzT3+dPn167KFD3zs7O61ejbfflj0xotLDUV2WfucQEZUCu3fvzsrKat68eVhYGIDu7HVf6tWvXx/AvXv3FApFVFSUVqvlTKa5y8rKioqKioyMjIyMPHfuXHR0dF5e3uNPsLOzy87Onjlzpr+/f82aNU2UpoHGjRun0+kGDx7cpUuXTp06vfD5x44dK1eu3Ndff/3555/36NGjIvsrGa0svb1WqyGVE5aWssc+derU5s2blUrliBEjUlNTDx48eOvWrdDQ0NDQUABVq1b18/Nr06aNn5+fq6vr4wPnzJmzbNmydp07W61a9fAMUqKX15BXX83San9OSbmUk7O0dm1Tp0NE9PLbvHkzgAYNGpw4ccLV1dVHOvacSjFpqvbChQvVq1ePj4+/cuWKZzEsL6XilpSUNHHiRI1GExkZee3atcc3kwNwdXWtU6eOi4uLWq3OyMiYN2/eoEGD/vzzz3fffffYsWMVKlQwVdpFtXHjxiNHjlSqVCk+Pt7f33/hwoWjR49+zvMPHz7s5+f36quvtmvXbt++fV988cUPP/xQYtm+rMpSSa/VQrofptXKG1ij0QwZMkSr1U6ZMmXWrFnSBy9evHjo/yUlJa1bt27dunUAqlevLtX2Hh4e+/btW7BgQUhIiL+/v7wpEZVaVkrln+np6YWF57Oz6/I2FhFRccrLy9u1axeA7OxsAD169DB1RvRidevWtbCwuHjxooODA4B9+/axpDc7Op2uXr169+/flyp5tVrt4eFRt27dChUqaLXajIyM6OjonTt3av+/Kunfv//69etbtWoVFRXVtWvXvXv3WhbDHKTscnNzJ0+eDCAwMHDevHnlypXr1avX84e0bt26adOmJ0+e7NSpk4WFxY8//jh48OB69eqVSL4vrbLU8f7GjYfr7QcMQFISpkzB778X9cTIp5o9e/aUKVPc3NyioqJsbGyefEJcXNy+ffuOHj164MCBpKQk6YMKhaJly5Z79uyxt7c3PgciM/J9UlLIrVudnZ1nmGhpmb8/vv4aNWpg5UpMmmSSFIiIitH9+/ejo6OPHTu2Y8eO8PBwCwsLrVar0+kiIiIaNWpk6uzoBTQajYuLS3p6ukLx8I16o0aNAgMDe/Xq5eLiYursSC9Hjhxp3bq1tbX1jBkzrl69mpSUFBUVlZKS8vhzLCwsvL29fXx8fHx83n///Ro1aiQlJTVv3jwxMXHgwIE///yzqZLX39dffz19+vT69etrtdro6Oh58+aNGzfuhaPOnDnTtGlTtVrdq1evNWvW+Pn5HThwoASyfZmJsiYrS3z5pWjZUgAiMND4eNevX7ezswMQFhb2wifrdLqoqKhFixa9/vrrANq0aWN8AkRmJzEvr0lERMuzZzM0GpMkMHKk+PhjceeOmDHDJNcnIiqy2FghhMjNFcnJ4upVodEInU4kJDz8bEaGOHBAzJkjunUTHTrM+9ebPWknto2NTW5urglfAukjMzNT2o1sbW3dt2/fnj172traSl9HtVrdsWPHNWvWPHjwwNRp0gtIrbUmTZoUExPz6CfRwcHB19d38ODBCxcuPHLkSE5OzpMDz5w5I1UWs2bNKvm0iyQxMVFKddSoUQDeeOONvLw8Pcf2798fQKdOnV555RUAmzdvLtZUX3plr6Tv3FkAIihI2NsLQGzZYmS89u3bA+jXr1+RRm3atAlA9+7djbw6kZkaeuVKo4iI327f1n/I+GvXViQn705LM/ii+fkPH4wZI44cEbNmsaQnIrMxdKgQQly5IhYsED17iiVLhEYjhg8XgYHC01MolQJ4+F+bNuH29va+vr6jRo0KCQmJjo5OSEioXr06gPfee09jonuppI/ExERpI33FihWPHDkifTAnJ2fTpk3+/v6PVmJbW1v7+/uHhIRkZ2ebNmF6qsLCwkqVKgGIjIzUaDTTpk0LDQ2VttPrM/zPP/9UqVQKhWLdunXFnaoxAgMDpd8qzs7OAHbs2KH/2Fu3bjk6Oj66HVCtWjV+Mxuj7JX0Z88KCwuhVIqRIwUgKlcWRlQIa9euBeDs7Hy7KJWJEELqOtu2bVuDL01k1valpzeKiAiKjtZ/yJfXr1/Pzb1fWGjYFbdtE7Vri/PnhRBizBghhBgxgiU9EZmNd94Ry5eLr78WCxaIMWPExIkiIUGMGSOcnAQgrKxE06Zi+HCxerWIicnVarX/Gh4dHS013Boq3Rug0iciQlenTjsAXl5e169ff/IJ6enpISEh7dq1e3QemKOjY2Bg4I4dOwoN/eNIxUFqYOHh4WFwhPnz50v3bo4dOyZjYjKKiIhQKpVWVlb9+vUD8Pbbbxc1wpw5cwB4enpKu4G+/PLL4sizjCh7Jb0Q4vPPBSBcXUWrVgIQffoYFiYtLU26A7d69eqijo2IiADQsGFDwy5NZO4Kdboes2ZZWFk9/2+VVoiTGRmTYmNDUlImxsb+lZYW+7RVai908aJwcBCAGDdObNokwsOFECI1VUREGJY+EVFJGzhQ3Loljh9/WNKnpYmRI8WYMWL7dnH69H9XIT3H4cOHra2tAcydO7f486Wi2b1bODgID4+sd98NuHfv3vOfnJCQMHfu3McPL7C1tX00q08mJ01ff/3118YEGTFihDRxeOXKFbkSk4tOp2vVqhWAwYMHW1hYqFSqqKioogbJz8+vXbs2gNGjRysUChsbmxs3bhRHtmWB0tA9+OZs2jTUq4e4OLi5wc7uxo0bu7ZvNyDMp59+eufOnbfeeku6O1Uk0p3ye/fuGXBdopeAWqFwzcwszM9fuXLlU59wu6BgRXKy//nzw65e3Xfv3va0NGe1+h0nJ9endaB8vqwsdOuGzEx07YrffkPPnrhyBRoNKlYEu0QRkbmwtoaLC5ydH/6vkxMaNACALl3QuLFe5/O2bt1648aNKpVq0qRJISEhxZgrFdGiRfD3R2Ymmje327ZtU/ny5Z///GrVqk2YMOHcuXMxMTEzZsyoXLlyTk5Onz59SiZber68vLzt27cD6NmzpzFxFi5c2KVLl7S0tI4dO6ampsqUnTw2bNhw9OhRFxeXuLi4wsLCIUOG1K1bt6hBLC0t582bB2DNmjXvvfdebm7uJLYsNpip7ymYyJkzwsJC6+KyevJkAJUrV757926RAhw6dEihUFhZWV28eNGA66enpwMoX768AWOJXg6xsbFKpdLGxiY9Pf3RBzUazY4dO6ZeuNDkzJlGERGNIiK6RkevvXXrXmHhBYM2Wel0IiBAAKJOnYfrcpo1E3XrijZtRGqqfC+GiKiYZWYKIYRGI9LTxZAhYt8+odMJA34vLl26FICFhcVff/0le5JUVBrNw52gCoXhe8Fu3LghvavUc6s2FavffvsNQLNmzYwPlZmZKa3FePPNN/XvPFfccnJyatSoAWDMmDEAKlSokGrEO6qOHTsC6Nu3r9Rp79ChQzKmWnaU1ZJeiBvz5tV2cnJzc3v77bcB9O7dW/+xeXl50gGhBq+o0Wq1SqVSqVSySw2VZVJ3ycWLFwshkpKSgoODa9asCaD32rXNz5yZFBt7MiPDyLcnS5dGKpWifHkxYIAARJUqont3AQh3d5GRIcuLeJllZGRs2bLln3/+MXUiRPRf27YJQDRpYniECRMmAHBwcDh79qx8edHz6HSioEAIITQaodH89wbNlCkCENbWYsMGo+JLh9s9dQc+lbD3338fwMKFC2WJlpiYWLVqVQBNmzaVJaDxRo8eDcDHx8fb2xvAggULjIl29epVKysrpVI5ZMgQALVq1crXZx8R/a+yW9IXFhZKzRgGDBjg4OAAIDQ0VM+xn3/+OQB3d3djbphJa+8fn58kKmtCQ0MBVKtWzd/fXzpjCUDt2rWX/PLLPTk6/YSFhalUqiZNpk2cqAGEpaUYNUoAwt5eXLhgfPiXXEpKSpUqVQAoFApfX9/g4OBr166ZOikiEn37CkAEBxseQafTSSdIValShUVgyUhIELNnCyHE5s1i3z7h6SkuXhTnzonFi4Wvrzh61Nj477zzDoDff//d+FTJGBkZGdbW1kqlMjEx0fhoBw4cuHjx4u+//65QKGxtbY0PKAsrKyupG3+LFi3c3d0LpJtVRhg3bpy3t/fhw4elMx32798vS55lSpncSw8AUKvVP/30k6Wl5Zo1a4KCggAMHTr0zp076enp4eHh0nMyMjISExOlx4WFhQUFBQASEhKCg4MVCsWqVausrKwMToDb6amMS05OvnLlikqlun37tnReS0BAQFhY2KVLl4YNGFBerTYyfkJCwocffqjVahs3vrtlSzsvr+zRo/Hjj1Ao8Msv8PKS5UW8tC5fvtyyZcuUlBQrKyup4+7kyZNr1arl6+u7YMGChIQEUydIVEbl5+OPPwCgWzfDgygUipUrV77zzjspKSnt27e/c+eOXOnRc9y8iZMncfUqAPj5Yf58ALCzw9Gj8PU1Nri0PDsyMtLYQGSc0NDQvLw8Pz8/aWrdSEOHDvX09Dx48KAQonHjxsYHlIW9vb0QIikp6dixYwcPHrSwsDAy4Ndffx0ZGalQKAoKCtRqtSz/dGVN2S3pAdSvX3/KlCk6nW7Lli2tW7e+d+/e/v37r1y5MnbsWOkJ69evnzVrlvT4+++/P3z4MABLS8uKFSsCSE5ONubqLOmpbNJqtX/88Ufnzp2rV68+depUrVZbUFDQuHHjxMTETZs2tWvXTqFQGH+VvLy87t27p6am+vn57du3Lzb2kI/P8OjoiwUFmDQJ3bsbf4WX2alTp958883r1683bdr05s2bw4cPHzduXK9evezt7cPDw8eNG1ejRg1vb+8vvvjiqvTm1HQ0QuxKS9uVlnYlN9e0mRCVjLAwZGSgYUO4uRkVx8LCYvPmzQ0aNLh27Vrnzp2zs7NlSpCeKS8PGRnIyQEAKyt88AF+/VW24CzpS4kNGzYA6N27t/GhIiIiLl++XKlSpRMnTgAYNmyY8TFlITULmDlz5q1bt6TVfEaytbVVKpXjxo0DMH36dHd3d+NjljkmXiVgagUFBQ0aNABQrly5jRs3CiEuXbrk7e0tffbatWsnT558cpR0VqSNjc1TP6undu3aAQgLCzM4ApF5uZ2f//O1a6+99pr0y8fKyqpXr16//vrrk03yjCctvalZs6a/vz+AevXqNW/eHMDgwbvZv+L5tm/fbmtrC6BLly7Z2dmJiYnSAchWVlYdO3YcPnx4QECAtFlJ4uXlNWPGjEuXLpkk2yyNJjg+Pikv7wG/rlQ29O8vADFrljzRkpOTpQ4mjRs3liciPcO/Ft6PHSuEEB9+KH76SZ74MTExAKpXry5PODJISkqKSqWytLRMS0szPppU4vbv31+hUNjZ2WVlZRkfUy5dunQBMGDAALkCrlq1CsBrr72WbVAvZCrrJb0Q4rvvvpO+h4rUjEFq4VClSpWbN28adt2AgAAAmzZtMmw4kbl4dLZ80zNnGkVENOjcuVatWsHBwbdv35ae8HiTPFkUFBT07NnT1tZ26NChAJycnKSjfapXr37nzh25rvJS+vnnn9VqNYCBAwcWFhYKIe7fv79kyRI/P79HzQ4sLS07dOgwYsSIbt26PV7bV61adf369SWccJZGE3Tp0vKkpPhS0wqYqPjk5+e3bt29VaufL1+WrbH56tWrFQqFo6OjXAHpqfLyhHTk9u3bIj1dXL4s7twRmzaJiAh54mu1Wnt7ewDG9B4nI0lzfu+//74Qwsgeb1qtVlp/PnjwYAB9+/aVKUd5xMbGWltbK+mZKQ0AACAASURBVBSKEydOGB/twYMH0mz/BiO7RJZhZb2k1+l0UpO8JUuWFGlgQUGBn58fgIYNGxp220z6EV2+fLkBY4lKs/TCwnk3b65ITg7PyFienPxuVJR0HF2LM2c+i4s7dfPmv07ZkZrkeXp6ypvGp59+amFhIZ3ADMDa2vr06dPyXuIlExwcLBXnkyZNevKzd+/eDQkJ8ff3f7RrTqVStWjRYtCgQQEBAY6OjgqFQq1Wl/ApO1kazQ9ytCAiMgs7d+4EUL9+fRljTp8+HUC/fv1kjEn66NdPAGLZMtkCSovR2FrMhKpVqwbg66+/3rx5s6ura3x8vMGhDhw4AMDV1VXqKr9z504Z85TF5MmTATRq1Eir1coSqkWLFjyF0WBlvaTftm2bNNmek5NT1LFpaWlubm4tW/bq3TvHgO9AqcwINqZlLVGplJiXt/bWLSHElZych2fLnz+/OiUl7RlN7AsLC6W7s8eOHZMrh7i4ODs7O5VKFRgYKC0jX7VqlVzBXz4ajUa6yahSqV54n/HOnTsrVqxo3769+v9bGKrV6piYmEqVKgEo4RPvsjWa5cnJJXlFIhMaOHAgjDhA96m8vLy4DdAk5s8XgBgyRLaAn3zyCYB58+bJFpGKyNfXF0CdOnXatGkjbfrLMPS83EGDBgH4+OOPAVSsWNH4rvKyy8zMlNYRhISEGBMnLi5OmvA3ZjszlfWSvkmTJgAWLVpk2PCLF+84OgpAzJhRtIGxsbH169cH4O/vb9iliUqtxLy84VeuLE9Kul1QsCQx8dSDBy+85SXdoO3fv78sCeTm5kqrb/z8/BISEpo3bz5ExjdNL52srKxOnToBsLOz+/PPP/UfmJ6eLs3b161bVwjRq1cvACtXriy2TJ8uU6NZmZz8bUJCCV+XqIQVFBQ4OTkBiImJkStmdHQ0AGdn50I5zg2lIjlwQABCxrPGf/zxRwCBgYGyRaQiSkhIkFq7tW/fXppd/89//mPAD1dBQYGzszOAjz76CMDQoUOLI1vjrV27FoCLi4vBdy6EEN26dZPxHWCZVaZL+p07C956K9jLq54BU/SP7Nkj1GqhUAh99pBqNJqwsLCAgABpV2qlSpW2bNli8KWJSqdHs/T6i4uLk7FJ3oABAwDUrl37/v37Qoi8vDwjt7S9xO7evduyZUup44DBqySk9yvz5s2Tdv3JmuCL5Wq1Tc+caXLmTI7Ra/+ISqEHDx4cOnTo22+/bd26tTQBKGPwL774QpoJlDEm6enePaFQCBsbIdftFKkvunSPlUwlLi5OWrPWu3dvFxcXAEFBQUUNsn37dmmSX+pe+ffffxdHqsbT6XRvvvnms/br6ePo0aMKhcLe3j4pKUne3MqaMl3SN2smAGH8AqUFCwQgrK3FcxaMxMaKmTN/qVy5srRO1cbGpl+/fgcOHDD22kSlT1ph4eH794s6Sq4med9//z0Ae3v76OhoI0O99A4dOuTq6grg9ddfN75l/d9//w2gQYMGsuRWJL0vXGgUEfFPZmbJX5pIfvn54uTJ7JUrBwwY4O3tLR058cj06dNlvFSdOnUA7NmzR8aYpL+aNQUgLlyQJ1pOTo5arVar1bm5ufJEJIOcPHlS2vE3btw46cG3335bpAjSqjepFXe1atWM36xefM6cOaNUKi0tLS9fvlzUsVqtVlpTOXPmzOLIrUxRCCFQJv31Fzp0QMWKuH4d9vbGRhs8GCtXon59nDuHxw/V1mpx8CBWrMDWrWjV6tzhww3d3d0HDhwYFBQkHW5PRJLNmzcHBAR4enpKJ/EY5vjx423atCksLNy4cWOPHj1kTO+l1KhRo8jISG9v77CwMGkywRg5OTlSk7yMjAwbGxtZMtTTzPj4bXfvjq9WrXelSiV5XSLZXLuGo0dx+jROn8Y//6CgACqVi7X1nexsCwuL+vXre3h4hIWF3blzp0OHDrt27ZLlmpcvX/bw8KhQocKtW7csLS1liUlF0rUrwsNvLVkiuneX4XBvAN7e3jExMadPn27cuLEsAckwoaGhUln+6aefSkdrhYaGfvDBB/qMzc7OdnFxycnJ6du379q1aydOnDhnzpziTdc4QUFBP//8c+fOnXfs2JGdna3PL6i6det6eHisWrVq0KBB1apVu3TpknTvgwxn6nsKJvPmmwIQc+bIE62gQAweLCIixJEjQggRESEuXRJTpojKlQUgAGFjIz75JO/o0aPyXI/opVNYWPjqq6/CiCZ5t27dkjq1TJgwQd7cXlavvfYagO+//37lypVGtreR1K1bF0B4eLjxoYpkS2pqo4iIz+PiSvi6REbJzBSzZokvvxQHDohhwx6+XQCEUim8vETPnt+PHTto0KCOHTtW+t97VXLNaH311VcAPvroI1mikQFmzgwG8Omnn8oVsF69egBGjBghV0Ay2MyZMwHY2NgMHz5cenD8+HF9Bq5btw5Ay5YtpVvt586dK+5UjXT79m1HR0cAu3btunHjhj7l5+zZsx8dXPfbb7+Z+hW8DNRy3RowL/v348gRODtj6FB5AlpYYPlyRERg6FDs24c//kB8PFavBgB3dwwciKAgVKxoBfjKcz2il45are7fv//s2bNXrFgh7e4uksLCwh49eiQlJfn5+X3zzTfFkeHLp3v37gsXLjx//vyKFSvq1avXr18/IwM2adLk/Pnzp0+fbtGihSwZ6snb1hbAhezskrwokbGCg/HRR3B1xSefoFUrpKSgShUolbh7F1FR2LSpWuvWIw8flp5bsWLFJk2aODo6bt68edq0aVWqVJH6Zhlj8+bNALp3727sCyFD1a3rCSAyMlKWaD/88EN0dLRCoZDarZNpTZ069ebNm8uXL9+6dWvfvn3XrVvXuXPn48ePu7m5PX9guXLlmjVr5uPjEx4e7uHh4ePjUzIJG6xSpUrTpk379NNPx40bd+DAgYCAAABarVar1T5rSNWqVSdMmJCSktKyZUvp+WQsU99TMI2PPxaAmDVL5rCnT4uFC8Xo0WLGDHHwoAgMFJyVJ9LfoyZ5aWlpRR07cuRIANWqVbt9+3Zx5PZS+vXXXwFIR82rVKqsrCwjAy5btgxA3759ZUlPf4U6XeCxY73Xrk2/d6+EL01kuEGDHj744QexfbtQKv87UQ8Ie/u4AQPGjx+/cePGuMdWoCxfvhyAhYXFrl27jLn4lStXAJQvX57dQ00oPj4egLOzs5FxNBqNdDSyQqH45JNPSvPW6zKloKBA6hPk6en59ttvSw/0aQOck5PTpUsXAF999dXzn5mWltaxY0dHR0fXF6lUqVKFChUsLS1bt25twLus58vPz69Ro4a1tbX+FahCoVAoFKdOnZI3kzKrzJX0W7cKIcTdu+L778XevQ8/KNdh2KdPi7VrxcqVom1bIfcPC1GZ8M4776Do50quX79eeo/LvS1FIr2nr1KlijQJYPy/3pkzZwC4u7vLkl6RSOsC9Dxbu1Cn+yExcXly8qkHD4o7MaJn+uwzIR2+OGyYSE8XFSoILy8RGCgWLhRHjohnH0M9depUALa2tnqu432qWbNmgQdHlQJSZ6WbN28aHCErK+u9994DYGlpuWbNGhlzI+NlZGRIW9Latm0rHWu3atWqJ5927969I0eOLFy4MDAwsFGjRpaWlhYWFuXKlXvhz/j48eP/1UFTn1p63Lhx8r7M/Pz8atWqPd6Sw9bWtsJzWVtbjxw5Ut40yrIytPBeo0FhIebPR24u6taFlRV+/x3t2wPAunUo+jrfp1CrYWmJDz/Er7/+T5M8ItJTnz599u7d+9lnn61Zs+bxu8ve3t7SnqsnRUVFDRo0CMCSJUt8fbm3pQjc3NycnJxSUlL8/PwiIyNPnTpl5D9g3bp1bWxsrly5cv/+/fLly8uVpz6aNGly/PjxU6dOtWvX7oVP1gihFWL4M76jiErIpElYtAhaLTp3RoUKSE2FSqXPuK+//jo5OfmXX37p3LnzsWPHateubcDFueq+NLh7964QAkBkZKTU3KSoUlJSunTpEhER4eTktHXr1rfeekvuHMko5cqV27FjR/Pmzffv39+zZ8/x48cPHDgQQFxcXORjbt68+fgotVptZ2eXkZGxbt265s2bPyt4UlLS8uXLdTrd4sWLO3Xq9PxM7O3tLSwsdu/e3bdv32XLlo0cOVI6IU8WixYtunnzpqenZ1RUlFpdhqrL0uMl+Uc/ffp0Wlravf+VmbkiNVV17x6k/zIz8cknaNIEJ06gWjUAuHMHM2cCgE4nTxoPHmDXLpQrh4MH5QlIVKbodLpNmzYByMnJOXPmjDTl+0iFChUeL/K9vLyk85k/+OCD7OzswMBAqbAn/SkUikaNGoWFhUnl9+nTp40MaGFhUa9evZMnT545c6Zt27Zy5KivJk2a4NkvQSNEQl7exZycf7KyIrOy+lWuHJmdvSI5+T9OTjWKslCQSE6Ojpg+/b//q189D0ChUCxfvvzWrVu7d+/u2LFjeHh4UU+suH79+rlz58qVK6fPLTAqJjExMZ06dUpLSwMwZMiQpUuXdujQwcrKSv8I0dHR/v7+8fHxrq6uO3fu9PDwKLZkyXA1a9bcvn27n5/fb7/9lpycHBISEhkZmZGR8fhz7O3t69Wr5+Pj4+Pj06BBgzp16ty4caNu3brLly8fPny4p6fnUyNPmTIlKyurS5cuS5cutbKyGjRokOLZM4pCiPnz53///fddu3bdunXrpEmTfvvtN1le4J07d6RVPwsWLGA9bzImXiUgh7Fjx5YrV+7Jl+bionl8V5paLT76SIwdK27eFF26iBUrxNChDyM8emCkiRMFIKZMkScaUVkzY8YMAE5OTseOHTt69Ogvv/wyderUHj16NGzY0MHB4am/waRTTxo3bsxjeA0zZcoUAEFBQQDc3NyMDzhixAgAs2fPNj5UkVy6dAlA1apVpf8tLCz8559/Vq1a9dXKlR/GxDQ7c6ZRRMSj/+bExy8yYpkrUWmQnZ0tTd81btw4MzNT/4G5ubnS5HxgYGDxpUfPt2/fPuleqru7+6MyrHz58oGBgTt27CgsLHxhhL1790ptxlu0aMEmMqVfaGiog4PDo6XpFSpU8PX1HTVqVEhISHR0tEajeXLIxx9/DKBbt25PDSgdCG9lZSW1tn377bdfmMO7774L4MMPP7SzswNw8OBBI1+URJpQ6dy5syzRyDBmX9Ln5+dLB195eHj07t172LBhU6dO/e6773766aft23MOHhSRkSI+XjzaLynV2wsWiJAQ8eigK7lOvGrSRAD/3aJPRPrbu3evSqVSKpUTJkyoXbt2dHT0v56Qnp4eERGxadOm4OBgabNZuXLlVCqVhYXFpk2bTJLzS+D3338H0K5dOzs7O4VCIa0CNUx6erpOpwsJCQHwwQcfyJikPrRarXRvd9CgQb6+vo9OuHV5/fXGERFNzpwJiI6edv36+tu3z2VmZmo0oXfulHCGRLJLTU2VVt2/++67zykCNRpNdHR0SEjIqFGjfH19pRZWCoVijlwH+VIR/fLLLxYWFgA6derUrFkzANbW1vXr1390t9rZ2Xnw4MFHjhzR6XRPjbBq1SopQvfu3XNycko4fzJASkoKAHt7+127diUnJ+szJDk5Waq9n+x0o9PpWrVqBWDw4MFSg9uoqKgXBrx69aqVlZVSqfzkk08AeHt763Pz6PkiIyOld2KXLl0yMhQZw+xLeqn1q5eXl8nbe96/L1QqYWkpjG4aTVTmXL9+3dnZGcDIkSOlt5urV6/WZ+Do0aNhihbrL43ExEQAjo6O0sGBew29JRkbG1u7du0JEyZcuHBBmn/Yt2/fU6cdZJSUlLRjx44ZM2b4+/u/8sorAKR3P5I33nijV69e8+bNO3v/fo6p/0AQFZPY2Fhp1X3fvn0fL/+uXr26fv36sWPHtmrV6vGfCwAqlap69eoDBw40+Runskin033++fB69aT7j9IdmapVq0pnj8fExMyYMePx5gg1atQYNWrU2bNnHw8grWgDMGrUKH4RzcXx48elNTVFGjVt2jQAzZs3/9fNHenAGhcXF2nvzPDhw/UMOH78eCng66+/DmDZsmVFyudJUkt/2fvtUVGZd0lfUFAgfUdu3LjR1LmIP/4QgGjVytR5EJmbnJychg0bAujYsaOrqyuAoXpshjl69Gjjxo379eunUqmsrKzucNLVUNJCp/79+wOYOXOmARFOnTpVqVIl6c1K9+7d1Wq1NH3k5OQkLSI1+JSsw/fv70lLO/v/i4ozNJrjGRk/JSePu3ZtyL59/9qFYW9vD6BNmzZ79uyR/YQeolLr9OnT0jd/7969P//88//85z9OTk7/+ulwdXWV7nAdPny4SKv0SU65uaJXLwFoX3nlq4kTK1SoAKBp06YpKSn/emJ0dPSkSZMebwrr5eU1Y8aMmJiYXr16AVCr1T/++KNJXgQZRjqaJyAgoEijMjMzK1euDGDLli2PPpiTk1OjRg0AY8aMke6hp6am6hnwwYMH0vfV2LFjpT/TxqzO27JlixSEf3NNzrxL+p9++gmAp6dnabhJOX68AMS0aabOg8jcSMVk7dq1pZvNzZo1y8vLe+GomJgYaXpZOvdu/vz5JZDqS0k6/WjYsGEA3nvvvaIO37t3r9TpwM/P78033wRQrly5wMDAxxs1OTs7BwUF7d69u+DZ53I91ZfXr8fn5aUWFHx5/XrX8+cbP7YfvtPZsw4ODo/vRZTeW7zzzjtFfQlE5m7nzp0qlUpaqyIpX758u3btZsyYsWPHDm60LhXS0kTr1gIQ9vZi7NiCN97wcHbu2rVrdnb2s0ZoNJp9+/YFBQU9OkBE2nVfvnz5/fv3l2TuZDypgdzEiROLOvCHH36Q3iM9+gP6xRdfAPDx8ZFOxVuwYEGRAkrVU9WqVaUutgafJJefn1+rVi0AS5cuNSwCyciMS3qNRiOtTVq3bp2pcxFCiB49xr711sKDB3nzm6gIFi1aJM2vSu1VXFxc9D+eV1orLvVj8/DweNaeQ3q+mTNnPpqlr1KlSpHGhoSESBPyAQEB0kbQKlWqSCtIhRDR0dHBwcGPH4z3qPmTPndthBDDr1xZnpR0NjOz38WLjSIiWp49O/DSpe8SEnalpd3Izf3XVzwhIUGaLuB3ApU1WVlZ1tbWCoVixIgRW7ZsSZCOu6fS4+pV4e4uAFG1qhg0SCgUArjz1Vd6zkjl5eXt2LEjMDDQwcFBpVKdPn26uPMl2Um97gyofgsLC6WO90uWLBFCJCYmSltppDc/bm5uRV0Hp9VqmzZtCuCTTz7Rfx/+k4KDg6X1I8ZvyCfjmXFJLzVhcnNzKw3fSffu3ZNW/7JJCZH+wsPDLS0tFQrFxIkTFQqFWq0+dOiQ/sNXr14trVqsWrUqgMOHDxdfqi+xv/76C0DLli0rVKjg7u6ekZGh58CFCxdKU0ZBQUHVq1eX/rTHx8c/+czY2NiFCxf+q7YPCAgICQm5d+/ecy4x5/+jRWZmXs7O1ryoVpfWE167dk3Pl0D0cpAOo/L19TV1IvQMgwYJQDRqJLp1E4BQqcQPPxgQRtqkduTIEdkTpOIWFLTa0zNwz54ivMl5RFqD9sorr2RkZPTp0wdA165dpQ5Ef/75pwEBw8PDFQqFtbW1/t3y/+X27dvSgQt//fWXAQmQ7My1pNdoNO7u7tC7h1Zx27ZtG4C33nrL1IkQmY2UlBRpF/fHH38s/WEo6uL5nJwcaS+idPO7T58+xZTqyy0tLU2hUFhZWem/oU6j0QwdOhSASqWaMGFCxYoVATRv3vyF2/kuXbo0c+ZMHx+fR7W9paXl8ePHn/X8eUU8aq5z584ANmzYUKRRROYuICDAgPW3VBJSU8WtWyI3V4wbJ1q1erjw/o8/DAv20UcfAVi8eLG8OVIJeOMNAQiDu8JL98SDgoIeL8Xbtm1rcD59+/YF0LlzZ+kv+NatW4s0XHrfZcBmPSom5lrSS50eXV1dS8MUvRBCalAxY8YMUydCZB4KCgqkfddvvfWWl5cXgJ49exoQZ+TIkQDYJM8Y//zzT7ly5V555RU9V8Xn5eVJ9YOVldWECRNsbGwAvP/++0Vao3T16tXZs2dXqlTJ0tLSsC/9U3311Vdg610qY7Kzs6VDKJ+6RoZMadkyMXeumD9ffPedSE0Vbm7i1VfFmTMGx1u8eDGAjz76SMYcqQRoNMLSUigUIjfXwAgnTpxQKBRKpfLRgnm1Wn3+/HmDU0pJSZFOfpXeR1WvXv05bR3+5dy5cyqVytLS8vLlywYnQPIyy5Jeq9XWqVMHwKpVq0ydi0hPT9+0aZPUjvLgwYOmTofIPAwePBhAtWrVpN5sHh4e+q/3fhyb5Blpz549Uq9saQnfo1Xx/fr1e2ptn56eLp2FW6FChTFjxkhvL0aMGGFYj9KjR48CqF+/vhwvRQghdu/eDeDNN9+UKyBR6bd582YALVq0MHUi9ISgoIcPpCL88mWRlGRMvCNHjgBo2LCh0ZlRiYqPF4B49VWjgtSqVetX4J5CEVGxom1RDq57lm+++UZaYVe3bl3pnVi7x/j6+jZ6BulMDd49L1XMsqSX9oxVr17d4IORjCe1fWrXrp3UGkqasOJGeiJ9/PXXXyqVSq1WSweuOjo6GnOj9/Emee7u7myNpr81a9ZIv8ECAwMLCgri4uKkHe/SDnkAtra2/v7+ISEhWVlZ0pATJ07Y2NjUqFFj+PDhUvtlY1YnZWdnq9VqlUr1KL6RpE0Etra2pWQBF1EJGDfuUJMm7y5YwMXYpc/HH//7gXEePHigVCotLS1N+AaYDHDokABEy5aGjNXpdJ988kloaOjh114rAAQggB9atzbm8DlJXl7e+PHjk5OT+/btK+1/1J+FhUUp2ftMEoUQokhfQpMTQvj4+ERFRf34449Dhgwp0WvfvYuwMOzevS0treuuXdLHLC0tvb29Y2JiNBrN2bNn69WrZ8wV1q1D48bw8MDUqZg1S46ciUqfhQsXjh8/3snJKSEhYdiwYV27du3SpYvB0UJCQgYMGNC0adOkpKSkpKTDhw+3bt1axmxfVosWLRo7dqwQYtSoUY8a3Ulu3Lixffv20NDQ8PBw6W+EjY1N27ZtAwICPvjgg6NHj65du3b9+vVqtXrZsmXShjqD+fj4/PPPP0ePHn28eZ4xXF1dr1+/HhUVJU07EL3c8vLg4oLMTMTFoWZNU2dD//Ltt3BygpUV4uIwfbosIT/4YFNMTO3ffqtXv75SloBUAkJCMGAA+vTBunVFGyiEGD58+LJlyypUqBBmZ9coMfHhJzZuRM+esuR2/fp1Ly+v/Pz8xYsXP376rLW1tbSx7klbt2795ptvqlWrdunSJVtbW1nSIGOZ9IaCIaSuj6+99pqeZyAZS6cTJ06IL74QzZoJpVK6N5bWoEHlypUDAgKGDx/+/vvvPzovtGLFimfPnjXmasuWifBwkZEh1/1cotKooKBA6kx+7Ngx46OxSV5RaTRixAhtixY9VCrVD8/tuhwbGztnzpwmTZo8+pNhZ2cnnULv4OAgS59b6asmY1uvHj16APjpp5/kCkgkj8xM8ddf4uJFeaNu3SoA0bSpvFFJPhcviuhoGeMFBAhAcH7UvMyfL9RqMXVqkQdOnjwZgI2NzaRJkzorFPmAABIcHMTt23Ll1q1bNwD9+/fXf4hWq23cuDGAL7/8Uq40yEil+g5fRkbG9evXz549u3///s2bN69cuXLu3Lljx44FMGnSJCsrq2K8dn4+Vq/GypXIykK3bvjiC5w8CUtLtGuHoUOdGjb0ql49NDR0yZIl27Ztu3//vpeXl6en5927d99+++0TJ04Yc+Vt2/DLL7h6Va5XQlTqWFhYSKegr1ixwvhoNjY2UuPWgoIClUq1efPm1NRU48OWfj/++OOjHe/6j8rLQ48e+OEH5cWLa7Zu3Sutn38WV1fXiRMnnjp1Kj4+XlqTn5OTk5mZaWtre/DgQal/gZGk+wWnT582PlQxBSSSQWEhhg2DgwO2b8fvv8sYePNmAOjeXcaQJCsPD3h7yxivfn0AiIyUMSTJb+fOhw/+/BN79qBDB+TmPvza6e+bb74JDg62sLAYM2bMd99994cQC6ZOfd3RsVZm5u4zZ2TJ89ixY1u3brW3t5f21etJqVQuWrRIoVAEBwfHx8fLkgkZy9T3FERhYeH48eO7devWs2fPd955p0mTJm5ubs7OzlLXpSdVrFjR3t7+woULxZvWqFHiwgURFyeCgsS0aaJfPzFihPjgA+HoKE3Uz2za1NbWtl27dgsXLrxx44YQoqCgQLrRZW9vb3CfvGXLxD//CCHE0KHyvRai0icuLk6pVNrY2KSnpxsfLTo6GsC777771ltvAejbt6/xMUu/pk2bSr8VH+14z8zMfP6Q9HTRurUARIUK4vBhQy567ty5xYsXX79+3ZDBTxMZmdKgQXqDBhq5Ah4+fBhAo0aN5ApIJIOICLFsmRBC6HRi0KCHD4yWlyfKlROAuHbN+GBkHnbuFIBo08bUedBzDRv28MHQoWLyZBEUJLRaMXJkESIsWbIEgFKp/Oyzz6RZzNmzZwsh5s2bB8DNzc341cparbZRo0YAZs6cacDwXr16AejVq5eRaZAsTF/SS2XwUzk6OtasWbNBgwZvv/12t27dPv744wkTJjRs2BCAn59f8TbBkv7iCiEGDxZ//CH+vx2FAET9+mLSpNQjRwoKCv41SKPRBAYGArCzs9u3b58Blz15Uty4IYQQ27cblT5R6de+fXvId75ubGysVqt1d3dXKBRBj5oMv9SuXbsWHBws/T2WtGz5ec+eIjRUPPUkmqQkUa/ew6a70q3D0qCwUNjYCIVCpKXJEzAzM1OlUllYWLBfKZUiFy+KuXOFECI3VwwbJjIzRZMmYtMmI6Nu3y4AwftXZUpSkgBE+fKy3BSi4hIQIL76Snz1lRg0SEyeLHbvFitXaaZfvAAAIABJREFUipEjRXq60Od8mLVr1yqVSoVCMW7cOGtrawCfffaZ9KnCwkJvb28A8+bNMzLJlStXAqhWrZr+x9c9LiEhoUmTdl5eEX//bWQiJAMTl/SZmZkVK1YE0Lt3740bN/7111+nTp26evXq3bt3n3okUnx8/LRp01xcXAAsWbKkGDMbOPC/D+7fF+XLC39/sXy5SEh4/jiNRjNw4EBp3mzv3r1Fvey6daJrVyFE0e7kEZmj0NBQAJ6ennIFnDp1KgAnJ6eTJ0/KFdMsxMXFzZ07t2nTpm++eVm68WhjI/z9RUiIePBA7N8vtFqRkyPmzBGAqFtXJCaaOuP/1aKFAETRf18+k5eXF4Ddu3fLFpHIeOPHi/nzxdChIjJSfPONAIRCIUaNEkZMtUVFiSFDxNKlMmZJZsDFRQBCvsVSJL9/zdLn5ooxY0T//qJrV/HGG2LhwucdUL9t2za1Wg1g5MiRUvOaYY/CCSGECAsLA+Dg4JCcnGxwhg8ePJC6Gv32228GB/nySwEIHx+hkW2lHRnIxCX9rFmzALRo0WLo0KExMTHPf3JeXl7VqlUBTJgwQZoJv3r1anFldvCgmDBBfPbZw+nyohy5rNPppO2pVlZW27Zt02eIRiNCQ8XPP4t168QXX4gNG1jS08uvsLBQxiZ5O3bsUCgUKpXKgFtpL434eDFvnmjRQigUDxcV1aolOnYUK1aIxEQxe7YICRH375s6yyeMGiUAMWuWPNGSkpKqVKlSo0YNpVLp6+u7cOHCJOMOgiaSTWam0GrFypWienUxerSwshKA8PYW588XNVJiomjRQhQWilWrxJUrxZErlV4LFohffhGpqcbcDqLiNXv2wwfffCO+/17k54uUFDFkiHjjjYd/natXF4sWPWVJ3b59+6Rp+aFDhzo7O0t7CZ+c5vT39wdgzJrEiRMnSiWYMauec3PF668LQPz4o8ExSB6mLOmzsrJeeeUV6S6Unlsff/zxR2k7fdeuXQH4+vo+dTLf5HQ63ejRowFYWlpu2bLlOc9MSBDjx4v69YWNjWjcWKxbJ06dEkOH/neVANFLTGrlWqQ+q0915coV6UjV7777To68zN7Nm2LhQtGunRg/XowZI8aNE2fP/vcdRmmzdq0AxPvvyxDq/Pnzr732GoBKlSo9aqGqVCpbt269ePHixNK2PoHKIJ1O+PkJQFhYiJEjRa1aAhD29rm//lqkMPHxon9/MX++WLJE9ib6VNr5+IiICHHpkvj+e1OnQkVUWCjWrRN16z4s7F95RSxefOhRU6Fr167Z2dkBGDhwoDTn0aVLl8LCwifjXLt2zcrKSqlUGrYsMTY2Vhp+6tQpo16PEJs3C0A4OYm7d42MREYxZUk/Z84cAM2bN69RowaArVu3vnCITqfr0KEDgA4dOlSuXFnGjbjFQVoGrFKp1q5d+69P5eTkjByZXLmysLUV9vbCzk7Y2goHB7FkiTh1Sty4IVxdxY0bIizMJIkTlRBZmuRlZWXVqVMHQNeuXYu3xYYZ0unEmDHi9m3Ru3fpLekvXRKAqFrV2Djh4eHSnEaLFi3u3r07evToCRMm9O7dW3qHJNX2jRo1mjFjxjU2EyMTKiwUM2Y8PBb3rbdE9+5CrR5Up05AQMC9e/eePzQ6WsyYITw8xMaNYt48MXmy+OwzlvRlzqhRIihIXLjAkt6MhYWJli2FnV2Wk5Ozvb39qFGjpJvOwcHBAQEBr7/+OoC2bdvmPnuBvjHT7NLM6ECZ5g/feUcAYtQoWYKRgUxW0ufm5kr3n6Qpem9vbz3n2xMTE6Vz4D/99FNpy/qVUrzmbPr06VJV/8svv0gfOXHiRJ8+fTw9PW1sjjg4iCpVhLu7aNNGTJggtm8XaWkiK0sIIc6dE2+8IaysxI4dJkyfqNgZ3ySvZ8+eANzd3TMyMmRM7KUxZowQQixdWrpK+v9j77zDmsi6OHxSCF1pgl1BLIAVBEUs4GIFFRTsWAE7ChbsKDZ2FxXsXbF3XBQVYW2IFbAhVoqu9A6BEEhyvj8um+VDhTQI6LyPzz5sMvfOiYaZOe132GwsLETEitrR1asxMhJzc7Emzf4fcvnyZWVlZRLZKSkpSUxMJG68kpLSsGHDFi1aNH78eDU1NaGUoLGxsY+PTy12b1FQVM/Nm6iriwCop3dn7Vry5ezQoUNMTEyVA8vL+REROG8etmjxn1DvnDm4dSvm56OhIeXS/3J4emJUFC5ahDt34pw5shQioahjHjxIHjJkCLkrKSoqzpo16+nTp0ZGRiTlWf0Um8LCwubNmwPAyZMnEdHb23vAgAFm1TJy5EhEvHPnDpnPJauutFevsHHjCgFQCnkhN5d+27ZtAGBqakoCUefF0X0ls6y1tbWJWn6fPn149ViWYd26dSQ75ODg0LJlSy0tLSsrq0mTJu3ceeHTpx8KlgoE6OlZUZp38WLdWkxB8f+UliYUFUXx+T/WcpECKUXy/P39iUhMjWIcvzKPHqGXF4qm7FFHhIcj6UnatAk/fMBmzfDrVzx7Fh88kGS3HTt2kLmnCxYsINHhnJycwMDAfv36CeehKikp2dnZzZs3z8HBQUVFhbxIo9FatWolougJBYWMycjAoUOzzMwUGIwpU6Z0794dABQUFHx8fPh8PofDCQ8P9/DwaNasha4uX9h/6+GB4eGYl4evXiEiPn6M9+/jhQvy/iwUdUJWFpaUoKcnIuKsWThv3n+zmIKCKImyhkpMTIyzszO5W5H/du/evcaaHUQ8duwYADRt2rSgoGDw4MHfHR9WGX19feHgus2bN8vwIxQWop0dcjj46BHev4+//46ImJ8vhx77v//GCRMQEZctw9ev8dAhRMRjx/DFi7q2pI6hIWKN3wCZU1paamhomJKS4uHhsWPHDiMjo7i4uB8Nov8uw4cPv3HjxtChQ+Pi4r5+/RoQEEB61+snXbp0iY+P79Wr17Vr17S0tERfuHYtbNgADAYcPQouLrVnIAXFDykvT/n6dbm29lRlZWMFheYy35/H47Vp0yY1NTUqKqpdu3aWlpbt27c3NDRs3759hw4dDA0N9fX1FRQUvrs2KirKxsaGx+NduHChmnGYFLt2wYIFMG0aHD0qb1P+JSICwsLAwgKCg2H9eti5ExChb19o0QL09aFFC1H3QcT169evX7+eRqOtXbuWhFArk52dff369QsXLty8eZPH4wEAg8GwsLDo3Llzfn5+WFgYm81u167d+/fvaTSaTD8iBYUICAQHt2+fu3w5j8ezsLAwMjI6fvw4IrZt2zY7O5vNZpOjnJxedujQdfRoqDSzsoIPH6BrV2AwICYGOnWqa/Mp6pKCAvjtN1BVhe3bwdQU8vMhLQ2uXoXt2yE9HQCgY0fw9obJk1FBgbqaNTwSExO3bdt25syZ0tLShIQE0l9cPYhoaWn55MkTIyOjQYMGlZaWCkVkAKCsrKy0tLTy8QwGIyYm5uXLl61atXr37p0wui0TRo6E3r2hZ08oK4Pr12HPHsjJgYAA2LBBhiepmbAwuHQJ7O0hIgJcXeHqVZg0Cc6dg0GDwNS0Ti2pa+QSSNixYwcAdO/evX379gBwSkxVGERMSUnR1NQEAC8vLwBQUVF5//59bZgqE2xsbADg9OnTEqxdswYBkMHAc+eSxF0bHR3t4eFx7949Cc5LQUHg8fI+fhyRn1+LHSArVqwAgKlTp0ZGRn57jWIyme3atRsyZMj8+fMDAwOvX7+elZWFiKmpqaR5Z9WqVbVn28/B48cVutr1h/Bw3LED375FT0/88AFXrcKTJ3HGDDx0CAHQ2Bh9fLDGongulztp0iTyJTlEQvE/Jj09fc+ePQMHDmQwGOSrxWKxPn78SMKs9bmBi+Kn5+nTpwYGBgDQqFEjclkjkPaQ6Ojo6pe7uFTMp6xmLBZFQ4fNxj59EAA7dsSMjP97i8vFoCDs0AEBsHlzXsuW+n5+fmzSxknR0GjVqhUAvHv3TsTjb9y4IVZOlLBx40aZW+7pib6+uHMnXr2KQ4agry+uWIGrV8v8PDVw8yZev45ubjhzJr58idOm4fHjOHMmftPS9LMhhyx9WVlZhw4dPn/+TFL0hoaGb9++JQMYxeLIkSMzZ87U0NCwtbW9ePGipaVlZGSk8Fmt/sDlcjU1NblcbkZGho6OjgQ7/P47XLlyOSZm/Pbt28l4vGooLi5+9OjR1atXr1y58uXLFzqdrq6uvmvXrsmTJ0tkPgUFIJZnZe1jMptoaY2vjf0TExMNDQ2VlJQGDBjQvHlzVVVVOp3O5XKzs7Pj4+M/fPhAkqtCzpw5M2bMmIEDBz548OC3334LCwurh7/49QouFxo3hvJyyM8HdXV5WwMAABERUFgIo0fD5s3g7AxBQbBhA9jaQt++4O8PJSUAADQaWFiAkxM4O2e3aVP14slms52dnW/evKmmpnbhwgWinCoKubm5165du3DhQlZW1uPHjx0cHP76669Tp05NnDhRtp+RgkJ08vPzXV1dL126RP5369atY8aMIeLBNcJmg5kZfPgAXl6wdWttWkkhJ8rKwMEBbtyAVq0gMhK++73g8eD8eQgJuXzu3BgA0NHR8fDwmD9/PkmAUTQURo4cefXq1bNnzxKdIFEIDw/fvn17165dq7yuqKj4bR4+ISHB2tq6Nu53Xl6wcSPY2MCaNXLO0gsEYGgIlpZw+zY8fAizZ8OhQ2BqCjQaKCqCsXGd2lN31H0UYe/evQBgbGzcsWNHAAgKCpJ4q+HDhwPA0KFDydSi+jm/iqhQdO/eXZpN/P23AgCNRtv5PXlTgUDw/PnzLVu2DBgwoHJwpFmzZq1btwYABoNBxDMoKMSFy/2cnu6fnOxWWHgHUZCbex5RxpMjSTOY+je+poKCgoGBwcCBA8eOHTt16tSZM2dOmjTJ1tb29evXs2fPBoDWrVuTjD1FjZiZIQDWn5Kd4uIKJbycHCwrw5wcRMSCAuRwsKQEQ0LQxQXV1cl4r0IlJSWSrhQmLtLS0kxNTQGgadOm38qJiQiZDLRx40YAWERUBCko5EpQUBC5+okrYf3sGbJYSKPhtWu1ZBqF3ODx0NkZAVBXF2vM3QoEgpCQkD59+pAvkrq6+uzZs7lcbp1YSiED1qxZAwDLly+XtyHicft2hZ73/fv49m3FzyUldT26KyEBDxzAtDRExMhIzMurKPf79AlzczE5GXftqlN76pK6dunLysqIHp6HhwcAGBgYfHfcoogI1e9J/6SCgsI///wjQ2tlgo+Pj0yeF/fu3UtKa4TVMkVFRSEhIe7u7iSiQWAwGGRKU3R0NHkm8PPzAwA6nX748GFpPwzFLwmPV8DjFZaVpSUmjo+OhoyMQBlu/uLFCxJF3rhxY3Bw8B9//OHu7m5jY9OqVavv9jYrKio2adIEAJSUlCT25X5B7t699OTJgK9fd8vbEDEoKcFLl9DTM7JyuMfU1HTJkiXkPtKuXTvpJevDwsIAoE+fPjKxmYJCSiROt2zejJqaOYMHu2ZUKcumaMjw+XxX19mmpuFaWhWCiCISGRlpb29Po9FUVVXXrl1bawZSyBhSqjN06FB5GyIGN24gANrZydsOREdHBMAfCf/Fx+PVq3VrUB1S1y79wYMHAcDIyIjMka6x+7FGTp8+vXfv3ry8PAUFBTqd/uzZM5nYKUP69+8PAH/99Zf0W+3fv584OZ07d+7Tp0/lYuNWrVq5ubldunTpu3O8iFdPo9EO1730JMVPQVbW4dhYtX/+WRIdDbGxKqWlsmk8zsnJIR2ks2fP/vZdLpebkJAQHh4eEBDg7u5ua2trYGBAAls0Gs3Ly0smNvwiZGUdjo6GhISx8jZEEjgcTkhIiIuLS+PGjYWRnZ49e2ZmZkq/eU5ODo1GU1JSKisrk343Cgopkdil5/PR0XEiAAwfPlyCOdUUtcv797hwIa5bhwEByOHgmjV4+DDevo2JiVhtZou0W2pr6zx7JsmETzs7O6oKqWGRkJAAALq6uvI2RFTKy9HEBAFw2zY5W3L7Nqnsw9TU7x+wYwdevly3NtUhde3Skwcyf3//Zs2atW3bVlaPUBs2bACAfv36yWQ3GcLhcJSUlOh0em5urvS7lZeXKykpEX+GJOStrKz8/PyECflq2LNnj66qakG3brh9u/SWUPxqEIfw+XONhIQx0dHw9q2lQCDttBw+nz9s2DAA6NWrV2lpqYiriouLQ0ND4+LipDz7r0ZJyavoaHj9Wl/UBcnJ6OeHu3ejyP80dUBpaenVq1f19PQA4OzZswcOHDhz5oz0244cuWDAAP8XL4ql34qCQhr4fD6pqpNs+devX7W1tQEgICBAtoZRSMvcuVhSgoi4fDlGRlaMniN/mExs2/bztGnTp0/39fU9ceLEgwcPUlJSBAIB0Y5VVla+c+eOZKe1t7cHgMs/sR/z0yEQCIj8QeqPHNN6RmAgAqChoZwfFng87NYNAXDLFnmaIUfEFqWTEnK7UldXT0xM/PTp049mU4lFcXExkdBfv3699LvJlocPH5aWlpqamspEnuTp06elpaUtW7bMy8srLi6+e/du3759RVw7Z86cCSxWIzc38PSEsjJYtkx6eyh+HXR0ZuTnXyoouM7nF7NYLYuLH2Vm7tDT85Rmz40b2RkZ5bq6uhcvXqw8dqV6VFRUiIgGhVgoKRnT6WpcbhKPl8lk6lZ9m82G2FiIjoZnzyA2FmJiYNUqOHAAPn8GHx/w85OHyd9BUVHR3t5+1KhRBw4cuHPnzv79+3v37j1+vLSqjaqqO0JC4OlT6NZNJmZSUEgIIsK/UXsJaNGixaFDhxwdHZcuXfry5Utd3f/7TXfV1DTMy6u6ZtEiyMiA0FAwMgJHR8nOS1Ez5eWgrAwAoKcHmZng5gYFBZCWBklJkJoKyclZSUlH792rvILJZPJ4PAUFhfPnz1tbW0t22uTkZAAgnUoUDQIajda1a9d79+69ePGi8vyL+kleHvj6AgBs3w4iP8fVCocOwcuXoK8PixbJ0ww5Utcu/YYNGzw9PZctWzZs2DBSey89O3fuzMrK6tOnD5kVV6+4e/cuAMjKMLLbwIEDjx8/rqGhYWlpKdZyjZkzAQDc3cHbG3JzRXxM5/F4jx8/vnbt2q1btxISEsaMGXPkyBGxTado+LRpc/DNm86FhTd1db0yM7elpq4BsNPT6yDZbn/9BevWNWKxbkZEJFYWg6CoJWg0hopKDzY7isOJV1fXRSzncF4WFz8rLn6mHJurN/Ya8Pn/HX37NrRrByoqYGQEubnys/r7mJubHzhwIDs7m06nv3jxory8XMrosLk5nDkDz56Bm5usbKSgkAQpXXoAcHBwmDVr1v79+48ePVrlrRl9+8KDB1UXDB0K587Bjh1w/jycOgWTJkl8aorqMDGB0FCwsICYGGAw4OBBAAA1NdDXh27dQFs7XVd3rolJaWlpQUHB169f3759W1hYSKfTjx8/TjLtkkFc+rZt28rmU1DUCd27dycuPalkrM9s2lSek6MwaBBI8SWVAQUFhYGBHAC9P/4AJSV5WiJH6tqlX7Ro0f3794ODg2fOnBkWFibNfYtQXFy8fft2+Fchr75B5O4lDq9Wgbj0RClqwIABkgzumjkTWCyYPh1+/x2YTNi48YdHJiUdCw8PDg29ffs2m80mr9Hp9GPHjpmYmCxevFiiT0DRgFFQaN6q1fbk5Gk5OYeVlWf88Uf0hw/TJJsc+fEjTJ0KiLBlC6Nv3/a1YS3Ft7Rte6SkJJbHy05P/z011QeRS15n6Wro0enQsSN06gQaGsDlQlERZGQAAHC5UP8GBJqbmwPAixcvDA0NP3z48Pr1a6J+L8WGAADPnsnEOgoKyZHepQeAbdu2kcj7kCFDmjZtKny9VE0NOnUCAFBUBOEg6y9fYMgQUFCAcePAw4Ny6WsLDw+4ehXOn4c//oDwcOjRA5KSID8fXr+G168BIMbcfM+/1yBtbW0Wi0Wj0dq0aSNNFVJWVhabzdbU1CRK0hQNhe7duwPAixcv5G1IDcTHxx8+PMTG5sK2bb3la8nGjRu+fj0wY8YBJydRJ//9fNS1Sw8Au3fvvnfvXnh4+LFjx6ZPny7lbnv37s3MzOzdu/egQYNkYl6NrFu3TkFBwcbGxtzcvPrUUElJybNnzxgMhujl8dVQVlb28OFDGo2WnZ0N0mT+XVxASQkmTaqoAeNw/ru7l5bCgwcQEQERERATE2xhEfL0KQAYGBjY29uPGDHi7du3ixYtWrJkCZvNJkr+FL8U2tpT8/NDPn7MCgrKjorK++efV9u3b1+yZIlYm7DZ4OgIBQXg6Pjr1kfVDeXlqSUlMcXFMSUlMQyGhra2C5ebpKExUiAoQSxXUuqkotKVTtcC4AumF9NDbsPlyxUrEcHBAdauhZIS8PKS64f4Dp07d1ZVVU1MTBwzZsyHDx+ePXsmpUtvagpMJsTFAYdTcV2koJALsnLpy8vLAeDatWuVX1/+3Sx9cDB8/gwA8PkztGghzXkpqoNGg5EjK36eMgWmTAEAyMuD5GRISoKkJO2cHDtd3aSkpOTk5JycHHJgUlLSw4cPhRPpxCUpKQmoqvsGCHHpnz9/Lm9DamDx4sX5+V+NjU927ixPlz4hIWHnzp3l5eVz5rSToxlyRw4ufbNmzQIDA11cXDw9PW1tbVu1aiXxVqWlpSRFv3btWtkZWB18Pj8wMDA/Px8AVFRU+vTpY2Vl1bdv3/79+7NYrCoHR0VFcblcc3NzmcRH38XE8LlcExOTx48fg5TF/M7O0LkzGBnBmjWgpgZfvsDkybBtG4SGAodTcYym5vw+fUbNmjV06NDmzZuT12xtbXV0dKZMmbJu3ToOh+NXbzpsKeoMVdWdEyd2zsvLW7p0qb+//+rVq4cNG2ZiYiL6DvPnw5s3YGICx4+D1GU6FP9PZiY8ewbPnkF0dK6tIKnfDeE7TGaTFi02ZmcfZLFaNmo0WEPDgcN5kZt7nrzbLLcPKz0dtLTA3BzMzWHgQLCxAWdnOX2MGmAwGD16mH79qqSn1x4AXr78JOWGKipgbAyvXsGLFyBmPxMFhSyR3qV/8uSJr68vjUbr0aOHlpZW5bf4bdtW1KQWFIBAUPFqt27w/j1s2ACFhbBypcTnpZAETU3Q1IQePQBgHsC8f1/OyMhITk729/e/ePHikiVLoqKiJPtKUC59A8XY2FhRUTEhIaGoqKjyDNd6RWho6M2bNzU1NeVeJb148WIul+vq6tqzZ0/5WiJf5ODSA8DkyZODg4MvX748Y8aMW7duSXz32r9/f2pqqqmp6dChQ2Vr4Y8QCAQHDx68e/funTt34uPjIyIiIiIiAEBdXb1fv342NjbW1tY9evQgpciybaTv+vffJUpKqTY2L169CtHTk1aJwMgIUlKATgdvb+DxYO5cKC0FDgeMjWHECLC1hQEDBn2vBmHChAkMBmPy5Mm///47n8//888/pTKDoqHRtGnzwMDAKVOmHDhwYOzYsefOnZs5c2ZUVFSN5fdsNqipAZ8PXl7w9i0cOwZqanVj8k8NIty5AwIBDBwI1tYQGSl8R1W9L8O6sbKyiYJCKwZDjc9nA9ANDM6np/txuR+Lim7z+fl0uqqKiqmqqjl/dV/w6wrtGkyEu1evuw8e0Hm8/ObNN0RFyaA1wMEBunWjUvQUckYgEAAAXVgVLyZ5eXnjxo0rLy9ftWrVxmoa66rg7S3Z6ShqCT09PT09vSNHjkRGRj569CgkJGTUqFES7EM10jdQWCyWsbHx8+fPX716ZWVlVRen/PoVTp8GdXWYMQMuXYJXr6q8v47BKK0kuCMQCI4fPw4APj4+Ojo6dWHhD7h9+/Zff/2lpqbmS2T6fmHk49IDwL59+x48eBAREXHkyJGZRLZNTLhcLvEnfXx8pO/JFxEFBQUnJycnJycAyMzMfPLkSVRUVERERGxs7PXr169fvw4AampqvXv3trW1DQkJAdk10sOdOzQ2u4VA0OLePbvRo0HSW/5/cDigqgoAwGQCAGzdCocPg+43UtjfMHbsWBUVFScnJ39//+Li4t27d9fZ3z9FfcDFxeXKlSuXL1/OzMzU0NB48uSJs7Pz8OHDGzduzGKxVFVV1dX7MZmKjRsDkwmNG4OiIqioQL9+cOQIqKhAZCQ8eSLvz/DTsGIF2NgAgwFLl0LTpqCmBmZmoK8PKipMRb6SUllxcQziQ3KsikoPgYDN5SZqa0/T1z/NYrVUUjKm0RgAAA1NoNDUlA4AKSkaWVmQkQHFxRUXM4kpKoLp06F7d1i/HqimIgp5IWWWfsaMGZ8/f7awsKA6434C1NXVV61a5eHh4e3tbWdnx2SK/cROZekbLt27d3/+/PmIESNcXFymTp0qZXNZDSDCihWwdy/88w+sXQvJyXD+fJVDdquoZJeUVHmxU6dOc+fOla0tHA7H29u7pKSkmkhBaWkph8MBAEQ8ffo0ADRt2rT+TweobeTm0jdp0iQgIGDixIleXl6DBg1q3bq1uDscOnQoJSWle/fuI0aMqA0La0RXV3fEiBHk7BkZGffv33/w4EFUVFRsbCzJ3pO78p9//vnq1StbW9sePXpIHHoHLhcePQIaDTIzAQBkkvk3MID4eHj8GGJioH9/6CCGdLm9vf3ly5fHjBmzd+9ePp+/d+9e0T+aQCB4/vz51q1bBw4c6OrqKpHpFHJm3759kZGRd+7cIcPngoODg4ODhe82a8ZLS/u/48eOhX79YM+eetiX3cDJzYUhQwAAzp0DFxfIyIDYWLh3DwAYdHrZGBVUKldSMlJVNVdRMdfQsBcISnNzzxYW3tLWnipny6WDCNpFR4OJCbx4Ac+fg2SKJYWF0KgRAIBAAOfPQ58+8G8TKwWFHJDGpd+1a9eVK1c0NDTOnTsnkwnBFHJn9uzZO3fufP/+/eHDh2fNmiXucsqlb7iQgp28vLwdO3bs2LGja9euEyeJEaFuAAAgAElEQVROHD9+fJs2bWR/suxsaNMG1NTAyAjy8sDWFho3BsTKh0xkMEoqZekvX76cn5/v5OQk80vNwIEDSX+xiNBoNAaD8enTp+DgYMdfewwnDf//36yOcXZ2vnjx4m+//RYeHv6je1hJSUne/zNu3DgGg9G+ffvPnz9funRp9OjRdWx29Xz9+vXOnTunTp0KCwur/Lq2tvaAAQOsra1tbGxMTEzEu2ffuwfW1tCtG+TlwZcvEBcH4nQv/5CSErh3D1q0gK5dJVgdFhbm6OjI4XAmTZoUFBRUfel1Tk7O7du3IyIiQkNDU1JSaDSaurq6m5vbn3/+SSX5GyKnTp2aPHkyADg5OWlra/N4vMLCQhI6RbyRn8/Mz4fycigqgtJSGDcONDVh9GgIDgYDA5g3r8btKURj5kw4fLjiBxeXimBfmzakJZ4zpTerSXcGo5Hw8Pz8kISEUerqAzt0+FtOFssGRGjSBHJyYNIkOHUKtm0DT0+xN3nyBEaNgp07wdkZPD1h5kwIDYWUFNixoxYspqAQATabra6urqamVlRUJNbCuLg4CwsLDodz9uzZceN+Xc3nn48LFy6MHTtWV1f306dP4rZVd+jQ4ePHj2/evDE2Nq4l8yhqg5CQkHHjxpWVlXXp0sXExOTmzZu5ubkAYNasWXS7djB+PDg7i1JUKyrl5TB/PuzfD2VlsGABFBbC2bNVDmmqqppRXFzlxd69exPdblkZ8uLFC9IP7+Li0omM5/geSkpKysrKhYWFa9asKS0tdXV1PXTokL6+fnx8vNIvO8IOAFCuZGVl6erqAsD+/fsRMTQ0dPLkyXZ2dn369DEyMmratCnJAVYhOTl5//79AGBsbMzn8+X7EX6Et7c3AMyfP//8+fPu7u5VoqRNmjSxt7f38/OLjo4WCAQ1b+fjgwA4bRoCYJMmKMqSOuHevXvkHjN+/Pjy8vIq75KZ9j4+Pr169aqcxm/btm3Pnj1JFdn8+fNF+hugqGdERkYCQIsWLXg8nijHe3oiIrq54a5dtWvYr8Xp07hiBa5ejUFByGbjtWuYkVHN4WVladHR8Px5Y8R6euUUnUGDEADnzkUAnDBB7OXBwaisjADo6IhPnuCiRYiIPj7o5iZzSykkJScHly5FX1/085O3KXXEp0+fAIDJZG7btu3z588irmKz2UZGRgAwd+7cWjWPQi4QxXsfHx+xVgkEAiUlJRqNVlxcXDt2UdQK+/fvr9xkQafTBwwYMH/+/DFjxlwntz0AZDJx6FAMCsKCAtmc9cIFXLMGvbzw7VtcuRJtbNDWtvIfZzs720rY2NioqakBwPHjx2VjACIiDhgwAACWLFki4vGbN28mzmDXrl0BYOPGjTI0psEhZ5ceEc+dOwcAqqqqCQkJgYGB3zrwKioqLVq06Ny5c79+/UaMGDFu3LjQ0FCiwX727Fl5m/9DLCwsACAsLEz4SkJCwuHDh11cXFq2/L++1aZNm44fPz4oKKi67QYM+O/p1dm51q0Xh8jIyEaNGgGAs7NzWVkZImZlZZFARuXOFiaTaWVlRaIYZOGNGzeUlZUBwNXVtd6GZih+hJubGwCsXLlSxOPfv8fycrx+Hc+cqVW7fj3Ky7GsTPTDX71qGR0NHM672rOobli5EhkM9PbGadPE/lIdPoxMJgLg2LHYsSNqaOChQ4iIRUV47VptGEshEb6+mJCAiBgQgM+fy9uaWufjx4/t2rWrHP42Njb28fH58OFD9QunTZsGAJ07dy4pKakbUynqEpIIVVNTS01NFX3V169fAUBPT6/2DKOQLTwez8PDAwBoNNqqVasuXrw4evRoYdqZxWLlT5mC8+fjsGGooFDh2ysr4zv53M2JPJ6enl6BjMIKFy5cICnP/Px8EZdwudz27dsDgKenJ41GU1FRET0S+vMhf5ceEZ2dnQGgd+/e+/fvnzNnzuzZs93c3KZNmzZhwoRRo0ZZW1t369atdevWVSqOdHR06q0fWFhYyGQyFRQU2Gz2dw9ISEgICgpyd3cXdsXY2dlVt+PNm+jlhZ6eSKfj7t21YrQUPHr0iAzq09PTMzMzq1yEY2hoOH/+/NDQ0O/GiW/duqWiogIA811dUbRkL0V9gMPhkH/xt2/fir7q2jUEwF69as8uipr59MkxOhpyck7I2xBpCQvDp08REcPDxVglEAj+/PMuANJoOH8+tmyJANi5M/7zTy2ZSSEFc+YgKf4KDsbQUHlbU7s8ffqUFC2amZnt2LHD0dFRtZLko7m5+e+//55AAhz/jzAvEh8fX/dmU9QNRPF+9uzZoi+5dOkSebSuPasoZEhREc6btx8AFBUVT58+LXw9Pz8/KCjI3t5+SLt2/7nxQ4bgvHk4fDgaGODNm7h4Ma5aVRGZrisEAkHfvn0BwNvbW/rduFyuoaEh/Fu1LTpXrlwBAE1NzZEjRw5o1SrZy0t6Yxoo9cKlz8zMVFdX19PT+zZFXwUmk9mkSRMDA4M2bdrcuXNH3ob/kNDQUACwsrIS5eB3797t27fvWjW5IR4P/fxw3Tr098ecHJmV2ciU2NhYYShRWVnZ1ta2ckK+Gu7fv99MWzvD3BzHjhUr2UghR86ePQsAFhYWYq0qKUFlZaTTq68Np6hd0tI2R0fDly8e8jZEWjZvxvHjkcdDD5E/SllZGclnDhx4b9kybNwYAdDGBkVOCVDULadP4+nTWF6Os2b93FeNW7dukaTFoEGDCgsL/fz8GAyGpaWlm5ubs7MzqYOrnLcXxlI/ffpE3j18+LB8PwJFrfLu3TsFBQUGgxEXF/fdA3g8XkJCQkhIiI+Pj729PXmibt68eadOnbhcbh1bSyEuKSloaooMBm/gQJfIyMjvHsNNT8cdO9DSEmm0Ct9eWxvPnMEZMyqOmDED6zbTGRMTQ6fTWSzW+/fvpdyKlNCbmJh828NbI4MGDdJTVb2/aJFAQwNpNHzwQEpjGij1wqVPTU1VVFSk0+nW1tYTJkyYO3fuqlWr/P39Dx8+fPny5Tt37rx48eLz58+FhYXytlRUlixZAgCrVq2SzXbnz+PVq4iIJ07gzZuy2bMWePr06cKFC0+cOMHhcMRayH70qOLheswYlOje86NqCIpaYvToYiurk4cOhYi7cPBgBMBTp2rDKAqRyMi47e/f19d3irwNkZbNmzEkBHfuRA8PvHsXw8Kw+icBNps9fPhwks9cteogi1XRhE897tZrLl/GTZvw3j28fh1LS+VtzfdITcW1a3HVKnz2TLINjh07RlSjp06dSprXFixYIJSbVVBQGDx48Pz5852dnRs3biz07bt167Zu3bpu3boBwNixY2X6kSjqI7NnzwaAUaNGkf9ls9kPHz7cs2ePu7u7ubk5aWOsjJaWFvnCTJkyhVIsqs+8fo2tWyMAtmsnWhH9ly8YEIBWVgiAr179JwAzb17d38+mT58OACNGjJBmk/T0dBKavHXrlgTLk+LjeS1bIoOBs2cjAPbo8WuW/dYLl97T0xMAnJyc5G2IzDAzMwOAiIgI2Wy3eTMmJiIiPn9eD6vuZUNMDGprIwAOH46iRQRKSkrCw8O9vb3NzMx0dHR69uwpok7br0N2dvaePXtGjRp14oQsq6wzMlBBARUUMCtL7LXbtiEAurjI0BwK8cjPz6fRaEpKSmUNvChm82bMyMAlS3DiRBw4EAFQUxNdXDAk5DtPNdnZ2ZaWlgCgra29cOFCOp3ev/8rD486TmlQiEluLo4di/36oZERAlQ0WtQ35szBggLk83HyZAmUa/38/Eirmre3d2W/Kycnh1Tbslgs4qGRvP3cuXNdXV2bNGlCXlRXV2/Tpo2selkp6jMZGRmklMPGxqZjx47fTg7W19cfOXKkm5vbnDlzJk6cuHHjxtjYWKJhtn79eonPm5VVEa16+RJTUmT2cSgIt25VpLQsLTEzU8zFpFpnzRoMD8fYWJw1qxYMrIGMjAwSObp+/brEm5C4gKOjo+R2LFyIANinD7ZtiwB48KDkWzVY5O/Sp6enq6io0Gg0UYq0GwT5+fkMBoPFYslMZTQsDElN3dat+PixbPash8TGoo4OAuDQodUFGt+9O7tv35AhQypPqiAJjbFjxzZ0L0V6+Hx+dHS0n5+fra0tyfzQaDQ6nS5Dr5645Q4Okqx9+7ZiaAPlSskRIicTGxsrb0Ok4uhRzMnB7Gx0dcUNG9DEpKIUkfj2ixe/vXr1aum/ed27d++yWKx27dqRwns6nb59e6B87aeoGT4f1dURAMeNQ4B6GtEWpsiWLMEXL3DLFvz4UZR1PB6P5F0ZDMbevXt/dFh2dvahQ4eGDh0qnP/MYDBsbGxWrVpFpj1NmdLgK24oRGTcuHFCASYmk2lsbOzg4DB9+nTSoGFsbFx5lnD37t0R8fr160wmk0ajSaxM/uYN7tyJiHjsGD55IsNP82shDLsVFCCHU1FTFh+PpF5s4kQpipAEArx+HS9ckFcd059//gkAnTp1kuwJPDY2llTv16gDWh15edikCQKgpycCoK4u5uVJvlvDRP4u/dKlSwHAQTL/oF4SEhICAP3795flpidO4Lp1eP68LPesh8THY/PmuGABImJW1n+/kMXFGB6O3t4kV7PK2po8lJuZmXl7e4eHhz98+FBLSwsA7OzsxC37/znIyMg4fvz4xIkTdXR0hHd0RUVFW1vbzp07k6dAWc0a6dEDAfDSJQmXd+mS17v3hZiYTzIxhkICJk2aBOKL0NR/EhP/q0YcMGAhAKioqNjb2wcFBbHZ7L/++svR0REAWCxWZfEhinpN//4IgHPmVIxxrVeQpPrcuZifj3w+urjgpk0VUSVjY/TxwR/r1XE4HCcnJwBQUlK6cOGCKGfLy8sjeXsy3HfTpk1hYWEA0KdPH1l9IIp6jrW1NQDMnDlz+fLlQ4cOJbOfKqOgoNCtW7epU6du3779/v37ZBWZ+sxisf7++28JTvrmDdrZoa8vjh5NufSSI5wvOWcObtqEZEzbggX4xx+4Zk39mUwtCWVlZR07dgSAbdu2SbC8f//+ALBs2TJp7di/HwGwVSvs2xcBUFa9zw0HObv02dnZpI7oaf0sqJMI0kewdu1aeRvSMElJQYEA//gDt2/HTZvw0CGcM6cijEn+NGnyduHCU6dOZf5/iVJsbCzxZocOHfqLDPLh8XgkIW9lZVW5Bk9fX3/y5Mnz5s1zcHBo1aoVl8v18/Mj6frdUqe54uIQALW0JA8Hk8TUhg0bpLSEQmICAgIAwNXVVd6G1Bbv3+PWrft69Ogh/KVo1KhR69atAaBx48b1WVqVoipLliAAurkhAJqYyNuaSnC5OGkSbt6MaWno44OrVmFsLN67hy4uFXW0AAjwcfBgX1/fKlr0OTk5RClaS0vrR1JY1UB8+6SkpNzc3J+jiYZCFFJSUhgMhpKSUn5+/pQpU4SdF2ZmZi4uLgEBAZGRkT96+Fm4cCH5vr0Tf+AZlaWXCTNnYkQERkTgzJm4aROuXo1Pn1YksH4Cbt26Re6zaWlpYi0kWsu6urqiD677IXw+9uyJADh7Ni5ZUj+lxGsVObv0K1asAAB7e3v5miFbunfvDgDUU6PkCAQodDZmzMAVK5DBQDMz9PbGyMhqKrbfvHnTrFkzABgwYEBRUVEdWSsP/v77b3Nzc01NTaHHoqysPGTIkLlz506fPt3IyKhy2P7x48f4b2UUjUYLDJSq3njjxoqcmcSQiSMizoOgqA2ioqIAoGvXrvI2pNZJTk4OCAiwsrKi0Wj6+vqNGjV6/guMN/+pOHcOAXDYMFRURDod64lKbkEB2toiADZqhOnpVd/l8TAyEj08UFd3vbU1uQ4bGxt7e3tHRkYmJSV16tQJANq2bSvWENDvQsY+Ud/qXwF/f3/4V3YqKirqwoULHz9+FFH3js/nOzg4AICBgUGGmMMjEhKQVDX99Re+fi2+3RSIiOjqilFRGBWFrq64aROmpOCMGThvnjxMefcOw8Jkfi21s7MTN1XA4XDatm0LAAdl1foeGYl2dvj+PZ49i+vX//SjT6sgT5c+JyeHpOijoqLkaIZsycnJodPpSkpKv0iiuFbg89HdveJnV1fMzMTcXBGXvn37lpSi9evXrwGNSBALDodDJkSQ2/P48ePd3d0dHR0rDzpSVVW1t7ffv3//58+fhQv37t1Lo9FoNJpkxVH//FMhxXLsGErT8cRmsxUVFRkMRk5OjuS7UEhBSUmJjY3N8uXL5W1I3XHjxg3iRMnbEAoxSUyskN8wM0MAvHdPVhtLfv1JS0NTUwTApk0xJqa6I8vKHoSFzZgxQ1tbu3LBCAD06NEjNTVVQgMqMWHCBAA4cOCA9FtR1HOI7vIlSXveSkpKevXqBQB9+/YVqz8xM7MiiB8SgpUeKCjEo0rhfUYGRkaipWWd23H1Km7ahFFROGmSbHvvP336RB5Ng4ODq7zF5/OFSfj4+Pi//vqL/LxhwwYi+iBjceubN/HQIXICbOCaQWIhT5d+zZo1pExajjbInODgYACwsbGRtyENnJUr8dw5PHQIxXc+379/37JlSwAwNzfPFTkWgIg8Hu/hw4f29vbe3t712dt8/PgxqaALCwsjKRoCjUYzNTVduXLl/fv3fzTY88CBAyQW4OvrK+55L1zAAQOwtFSMMeA/YuDAgQDg5eVVWj+nUlH8dPB4PKL8nCm2pjCFnGH37Pm2X78LHh7jOnbcIVE4sgo5OTlWVlYA0K5dOx8fn/gfd7x/C//duwpF5Y4dMSlJxFU8Hi8yMtLDw0NbW7tZs2ZEo76kpET6B9lt27YBgJtQoo/iJ+Xjx48kHiRNuig1NZX0Hzk7O4uY3mez8eNHHDwYz57F3burUYegqAHh1SIpCdPSKuTxEhLq3A5394rG/UOH8NEj2e5ta2tLp9Pt7OzI/06dOpXIhH/48KF9+/bkxTNnzowfP578fOTIER0dHdkXNfv64pcviIhPn+KvFO6Um0ufn5+voaEBABI0ktVnPDw8QLp5IRQVPH8u8d0jOTnZwMAAAMzMzLKzs6s/ODMz8/z58y4uLkRgDwDodLq5uXm9TfJv2bIFAGbPns3hcFRUVLS1tZ2dnffv3//161dRlp88eZLJZAKAt7e3iGd8/x5378bz5/HAAdy0SQYu/R9//EH+qjU0NFxcXMLDw6mpuRS1Tb9+/UC6QTsUcmHo0KEAMGfOHJDFAPbk5GTSmlRZG7xLly7r169/8+ZN9WsfP35s2b49V18fe/USf94UIuLz589JKGH06NFMJlP6QT8PHjyAf7XNKX5ifHx8AGD69OlS7vPmzRvy7L169ervHpCbi5GRGBCALi5obIwMBu7bh/7+OGcObtlCufT1lTNncMMGFEVoc/58JFGh7dvx1SsZmsDj8UxMTABg8eLF5JWkpCQ+n4+Iubm5Qjnea9euLagkISCz0WCVOXkSw8MREU+cQFlNE28IyM2lX79+PQD89ttv8jKgltixY4eFhcVPFqdoiHz+/JlksLt37571zfz08vLye/furVixokePHmQmMKFDhw729vbEt6+3pftDhgwBgLNnzyLiu3fvJMjznDlzhnj11UiM/v+QAQTAP//EBw9w7VocORLLylDiOoby8nI3NzcajaaioiL8m2/fvr2vr29xcrKEm1JQ1ISXlxcVb22IkII+MrhYX19fmq1ev35Narg6d+6cnJxMMue6urrCC5GBgYGHh8d37+BXrlxRVlYGgDVTp6KkmVIej6eqqkqj0ZydnQGgmtl1IlJcXMxkMhkMBpvNlnIrivoMURS/deuW9FvdvHmTPADs27ePz+d/+PDh/PnzK1asGDZsWI8evYVKxOSPggKuX4/+/picjIaGlEtfL4mIQOIwb9uGDx7UcPDr1zh7Nm7bhp6eiIiyy5CTwQr6+vryHztVVobr1+OGDfj77w17loCYyMelLygoIF7T3bt35WIAxa9AWloaCRkaGRmRrsX09PSgoCBnZ2cSpSYoKyvb2tr6+fkJyy+TkpL09fVJkr++VeCXl5erq6vTaDRxZUWrcP78eTLl2MvLq3KGPC4uzt/ff9q0vYqKlYcM4KRJuGMHPn6MRUVoYYEeHtiypSTity9fvhTqkKupqU2ePHn27NlEH8VYRweZTDQzw4AArKm2goJCXM6cOQM/nRrrrwCZC2ttbS1l68Tt27cbN25MOuMqqysLq+L19PSE9wV9fX3i25PL4+HDh4kXNGPGjB+1NYkIqfknRQczZsyQZitC165d4efSJKKowrNnzwBAV1dXyu+eEOJ9MZlM8jtVuX2vVavc/v3RwwOPHMHYWORyMTcXSSTh5El8+RI3bvylvKRa5ONHPHcOX7yQeqPff8ePHxERX73CHTtqPp7Px8JC/PwZnZwQAK9ckdoCLCwsbNq0KQCIOJKTojaQj0u/adMmoCSvKWqftLQ0Y2Nj4j2SILcQY2PjxYsXh4eHf7edOzk5uV27dgDQo0ePb5P8coRolRsbG0u/1bVr15SUlABg5syZt27d8vDwaNOmzb9hDi01tYohA+HhWOUpgsNBS0sEQCUlPHJE1NOVl5f7+fmRicp6enqVB+qam5vPmzcvbMECVFWtiCIoK+OECXj9OspWNIXiF+bTpyRLywA7u2r1zCjqH+np6QCgrq4uTevE6dNoaTkKACZMmMDlcr97THl5eXh4uLu7e5MmTYRXJ0NDww4dOhBvRyajN8mYW3d3d1LwL/2Grq6uABAQECD9VhT1E1Jh5CF9zxtiYWFhYGBgWlqamZkZ8cGaNWtma2vr4eERFBQUFxfH//FQIT4fu3RBAPTxkd4QCnRyQkdHWRSGX7qERHDuzBm8cUOkJcuWIZOJCxciABoYoNR59SVLlhC3jmqilCN16tJzudyXL18ePXqURMojfqUOBwp5kZmZKfQeVVVVbW1tAwICkkUo8P7y5Uv79u2J/ywTaWKZQMJh82Q0+SQ0NFRRUZFUkxKaNm06bdq0s2fPZmdXd4kvL0dv7wrv28Wl5irUuLi4nj17ksdid3d3MmIwOjraw8NDR0eHnLrEyAhtbNDNDa2tkU6v2P3iRczPx6QkKi9AISUCAWprI0CFbg5FA6JVq1bk6iTZ1c/fH2k0VFUt8vHZLsoTJ5/PJ3l7MhWVwWAwmUxhL6iUnD59mlSLKCgoyKRgnmRcJ02aJBPzKOobfD6fdIs8koWYWVBQEAD079+fTGG4J+YIievXkclEGg2DgqS3hUJq9uzBrVuxvBw3b0ZfX/zjD1GflDZsQAA0Nq4I0mzeLI0Vnz6VGxoOptPpMdVPAKGoZWTm0vP5bMQqsT1+aemn3NwrGzduHDdunImJCSn0BYCWLVs2bdq0mlggBYUMSUlJCQgIOHbsWFlZmVgL09PTO3fuDACdOnVKSUmR4NSfPn2SYFU12Nrayqq0KTAw8OHDhxMnTqTRaAYGBps3b46NjRUrwnrsGCorIwAOHsz9559/vntM5eR827Ztvw3kcTicc+fOrZ0yBZnMCjdeSwunTME5c7B3b7x+HZcuxSNH5DS/leKnYsiQijARRcPC1NQUAMi0jspV8TUuFAhw2TIEQBoNf/9d7PPyeLzg4OBp06YJpy5JD5Eub9q0KWlBun//vpQbxsbGAoBQUJriJ+P27dsA0KZNG5nkP4nY5Lx58yQu9ztwoKLH/u+/pTfnV+fmzZu3b9+WcPG7dxVPYH/+iYmJ4q0tLUVDQwTARYsQAFVUpJlP6OCAioq4ciXlz8sZ2bj0WVmHv371zszcU1LyMj3dPylpenx8z9hYlehoiI6Gzp0r+tMYDEb79u0dHR1JXu7UqVMyOTsFRe2RkZFBOhU7dOjwI6+1CsXFxeHh4R4eHi1atAAACwsLWZXuc7lcIq2UkZEh5VafP38GAC0trT59+gBAaGioZPs8f46GhmhmtlJHRyecSIxW4s2bN+bm5sLkfA1yg+npuH079ujxXxP/ypXo5oYk9rd5M8o6PkLxq7F6NQKgyKMeasbd3V1bW7tDhw67du2qP7U8Pxl+fn7kEWLQoEFubm7Cuh4A6N9/2sqVPxw8zOXi+PEIgCwWnj5dt0b/GIFAQBKkkyZNAoCtW7dKuWFZWZmysjKNRhNraCtFQ8HNzQ0AVq1aJf1WmZmZTCZTQUFhzJgxALBZ0tysp2dF7P39+yLprfqVcXV1PSJ6+2JlysuxVy8EwHHjUEsL1dXx3Tvxdrh0qeJfccQIBMDJkyUxA/HvvxEA1dWRugHKHdm49Kmp67OzjwsE3NTUDcSNJ39evWr14cPQ48c3BgUFxcTEkHGaGRkZvr6+JND+o5Y2Cor6Q25uLvFL27Ztm/DjKaIvXrzw8/OztrYWVqMAAFFU6tKlS3p6uvSWREZGyqr98siRIwAwcuRIFovFZDILCgok3io7u4CI8DOZTOHjKY/HEybn27Rp8623Xx1v3qCPD7Ztiw8eoJtbRSGZn1+FAAwFhaT89RcC4MCBMthKIBAQJXbhyAw6nd6vX7/AwEARx0lS1AiPx5s1axbJB+zbt0/4orAq3tr6Fon+tWmDHh4YGYkCAX78iAIBCgS4ezcCoIaGDEWdZcPgwYMBYO7cuQAgHNEsAQkJCWSCg6WlJQCEhITIzkaKegGHwyEBoNevXxcXF4tbaViFXbt2AcCwYcPU1NRoNFqiuKndf+HzcfRoHDDgjoGBgfQJBgpJIJXzLVrgoEGkVFKS5sTBgxEAp04tNTZeY2EhQcUQj4dduyIA+vmJfXIKmSMbl14g4OXnX0tIGFNUFPn589zMzL1FRZE8Xt63Rz569EhNTa1Lly5EtEz6CS4UFHVAXl5e7969AaB169YfKzmWbDY7JCTE3d2dtHoKq1HMzMy8vb0jIyNTU1O7dOkCAB07dpT+QZ/EwiqP9JSYKVOmwL+qy7169ZJyN4FA4OfnR8piR40a9fz5cyJkBQAuLi4SzgLk8VAgwHvK+ugAACAASURBVNBQ9PHB4GB0d6fa6SmkJC0NAbBxY5Sy64vH4xF5MwaDsWXLlsDAwAkTJlTWjjY2Nvbx8ZF5380vRXFxsb29PQCoqKhcvXr12wN4PN69e8Vz52KzZv9V9nTsiKNH49GjWFaGixfjhg2yHb0sG1atWgUAM2fOBIB27dpJtklMTAwRF9i9e7euri6NRps7d65s7aSQOydOnKDT6Wpqamlpaebm5lKOSCDTFkjVfZ8+faTZis0uIfo4VlZWNQ4t4/P579+/P3fu3PLly7t3725iYnL06FFpzv7TIBAIrkggOP/iBbJYSKOhh0dF2FK0GtKqvH4t0NIKtrEhkVNTU1NxG6L37pWVvh6FDJCNS19U9IDNfvTp0yiBoAZ5ai6Xa2BgAACLFi0CgGbNmhUXF8vEBgqKWqWoqMja2pr0QB4+fNjPz8/W1pbFYgkf4nV1dZ2dnYOCgqpUP+bk5JA7X9u2bSUOihMGDhwIAJcvX5buoyAiEnF78ky5fPly6TdExCtXrjRq1EhYm9C6deuwsDAZ7JuSgrGxVWX3KSgkwsYGXVyw0ggzsWGz2XZ2dgCgqqp67do1gUDQpk0bJSWlYcOGLVq0aPz48d/69h8+fJDdJ2gwFBYWXrp0KTg4WIK1OTk5xP3Q0tJ6UNOkZT4fIyPRwwNbtMARI9DTEz09MTUVFy+WyO7a58qVKwAwcOBA0kiVLf7MzoiICHKxHTBgwIABAwCAxWK9fPmyNqylkCOJiYmk7m/hwoUqKioAsGXLFsm2+vz5M41GU1FRGTZsGADs3LlTStvS0tLIg4Szs3MVV7CsrCwuLi4oKMjDw8PW1pZMrRZCo9EaN278kSq7Q0REcVtvuFzuPicnvo4OurigujoC4NmzEp99lZcXANjY2JB/ysOHD4u+trQU9fQoeZp6hGxc+oKCsKysQ1xuzSriiHj06FFSdd+rVy8A+F0CyRoKCnlQVFREHp6EpbYkIe/j4xMdHV2NdA3x6pl0evyoUWKrmPxLaWkpaZiUvjP/06dPAKCtrU1+B2/evCnlhkKIID+LxdLS0kpLS5PVthQUEpOSgidPIiLeuoWxsejggAUFGBeHkslHZGdnE/kJbW1tMgk8Kyurb9++QuU2JSUle3v7BQsWODg4qKqqCh9hLSwsFi9eHB8fL9MPV38pKiqytbUl023atm1becZ7jSQmJpKZo/r6+u/EaRDl8zE7Gz09MT0dFy2qvy59amoqADRu3JiELcS9Ah8/fpy4eU5OTt27dyeB5tgfKQpQNHDOnTtHp9PpdPrKlSsZDAaNRjtJrmhismXLFgAYM2YMi8ViMBgyaQZ88+aNhoYGyfxHRERs3brVxcWlS5cuJKxfmZYtW9rb269evfrkyZP9+/cn9SmZmZnS2/CrsXLlSgCws7BImjwZAXDsWGl2y83NJeokZLimrq5uvggB7/T0irD49es4bx4WFSEipqRQlZRyRjYufVycUXQ0lJSIFCHm8Xik6p58gTQ0NChNF4qGQn5+fq9evTQ0NFxdXYODg0UvKc/Ly3s1ZQoCYKtWKFHK7u7duwDQrVs3RKyxzq16Dh48CAAODg4KCgpMJlPCwvhvOHfuHIPBIN4OidxTE0op5E5CAvr7IyKeOYP37uG4cbhiBT55gsePi71VYmIiGVGur6///v37ym9lZWUFBQXZ29sLn2UZDIalpaWbm5uzszNJqAJAkyZNyATHn5uUlBSiKqqiokKuBgQDA4Nly5Y9ffq0mivDy5cvibBo165dJWtW8vRERNy9u/669IhIPuPUqVMBQKxx9wEBASR+NGXKFENDQwAwNDSkEp4/N0QhUllZ2cPDg8QNSTxRLMivJKm6HzJkiKxsCwsLYzKZVXx4BoNhYGBgb2/v4+MTEhJSJb5fXFxsYWEBAP369SstLZWVJQ2RyMjIVq1aqampOTo6bt68OSIionqP+tGjRwwGg06nL1iwAACODx+O4tf4VGHPnj2krJL0Sy4W4brp74+uroiICxbguXN49y4i4uLFSFVdyxeZuPT82Fil6Ggany/qeNVLly4BQLNmzUgls0yUPCko6jtsNg4ciACop4evX4u+jsvl3r59m+RzZsyY8e7du9atW0tWzkogSsukkV7KhjohFy9eJDf1zZs3v3nzhmTnNm7cKJPNKSgkJiEBhw5FX190dsZ793DRIty2DffuxaNHcehQ3LZN1Nk9z54909PTA4CePXtWk+BKS0vbs2ePjY0NCW+RopXBgwfv3r1bSUkJAEQZvdagiY+PJzWcRkZGycnJwhnvxIkltGrV6rt5e2E9+W+//SaxZieZN8fjoQQ9qnWGg4MDAPj4+OzYsePt27eiLOHxeMQfYzAYS5YsadKkCQD06tWLSnX+CpD7tY6OjouLC4mbi9XOEx8fDwCampokQ37s2DEZ2ubv76+jo9OpU6dZs2bt27fv8ePHNXbUpqamEgWicePG/Zqhfw6Hs2zZsm/LGYhz5OzsHBAQEBkZSWTFCcXFxaR8yd3dXVlZGQCuXbsmvSU8Hq9bt24AQGYrVMHa2keoVCL8s2kT+vnhjRsVLv3q1XjyJA4fTrn0ckYGLj2XmxwdDS9fthBrFREbmz9/Po1GU1VVpWp0KX4JiovR1hYBUFcXa+p7TEtLCwoKcnZ2Ju4xSfE1a9aMRGdZLNalS5cks4I8W8+YMUNWAbXg4GBSCOrr60teuXHjBikRPCtFlxcFhfRUydIvWoRlZThwIC5d+t8DirEx+vhUVz0THh5OXE1bW1sRXc2cnByStye1MJmZmZMnT4afXRT2+cOHpG/WysoqJyen8lvEt/f29m7Xrp3webFly5YeHh7h4eE8Hk9YT+7i4iKNsrdQJG7OHGk+Su0yf/58ANDV1b169aooicrS0tKxY8cCgKKi4tKlS0lb9ahRoyg1ol+E8vJyMijByMiINMN36tSpyq9YNRBFxkmTJjEYDCUlJVGKq2ubuLg48mzj4+Mjb1vqmqioqE6dOgEAnU6fPn36s2fPzp8/7+HhYWVlRXx1IUwm09jY2MXFJSAggIweNDIyIvJMriRRLgtIEaiuru73XPq133Xpv3xBV1d0d8dz5/DMGUxIQFdXyqWXMzJw6QsKwqOj4f17a7FWhYeHA4CGhgbRGZKJiDcFRQOgpASHDkUAdHP7zrvl5RgZid7eaGbWunlz4VW1a9eunp6eZJZe06ZNhYOdjotfPfz+/Xty7SZ3BfHGy32PmzdvkmF1Veq1/vjjDwBQVlZ++vSplKegoJCY3Fwk8mqvXmFSEpI5X8+e4cOHeP48jh2Lqqr/Pak4Orr6+flVmVUZFBREXM0pU6ZI4GpmZWURwfbAwEASSpPJ56qPXLlS2KSJaadOo0aNqpxcqoJAIHj48KGXl1fr1q2FlzgiFAcAy5cvlzJrN2YM+vqiry/OmiXNNrXL33//LRx3qqKiYm9vHxQUxGZ/v9QxNzeXJFc1NTW9vb1JZm/GjBnllGjor0RhYSEpnu/bty9Jq4peuE56NGbPng0ATk5OtW2qiNy4cYPJZNJotKCgIHnbUkcUFxd7e3uTGi4TE5MnT55UOaCsrCw6OnrPnj3Tp0/v3LmzsNpLiJGREQC0bdtWmtnD37J79+6UlBQRD/b3xy9f8M0b7NmTKryvR8jApc/M3BMdDcnJ3/NPquW3334DgFmzZjGZTBaLVc3EbwqKn4rSUty8GcvK8MQJ3LAB163D3FzctQvt7FBFReheLBk8eMyYMQcPHhQ2lBYXF9va2hKHnMyMYDAY4g6D2b9/PwCMHj2awWCwWKwfPUSKSHh4OCknXrRo0bfvkjqu5s2bU5O6KeotHA6GhKCLC7Zv/49Q+ZIo1b99+zYgIIC86OHhIaWr+fDhQwDo0qWLrCyXOfv27dPS0mrWrJlYanYV7N6NDAYC5C5ezOPVMPtGSFxcnI+PT4cOHYgA2KZNmySx+/9pEFl6RLx79+66detMTU2FD+vq6uoTJky4fPly5YCIQCAQjlAlo+xpNNovmNikQMSvX7+2bNkSAMaNG0cK18ePH1/j7ymHw1mzZk2/fv3MzMwAQOL6vtqAKPsoKChERETI25Za5/79++3btye5d29vb1HCMWw2OzIyMiAgwMXFxdjYmHj4NBrt9u3bsrJKIBAEBgaKVbiRn18xgygzE4uLkXyO3FxKHk/OyMClj49f9eSJQlqa2ML1z549I1X3pJxsypQp0htDQdFgSEzEtWsREePjccMGbNHivyJgb28MD0cu99tFJSUlpPpOU1OTlG7SaLRdu3aJftrx48cDAHk07NevnzSf4MGDB0TT+0dVNmVlZUQvw8zMjCoQpajncDilV65cmTx5srDVhfx+MRiM3bt3y2J/joKCAoPBkDKOVkucOHGCxWIJgxrEh/T09IyKiqrBZxAI0McHAZBGQ0ldzatXr3769EmytVVYs6bih9WrZbJfrZOcnBwQEGBlZSX8y1dWViZ5e6Jdevv27R49ekycOJGEcQ8cOCBvkynkRkxMDLntLly4kFyp1q9fX+OqkpKSvXv3AkCjRo2qqaCRC15eXgDQuHHj1+JoDCFiXl5eQ1HXzs/Pd3d3J7/gXbt2jY6Olmyfbdu2kZomGSpolJSUuLq6Nm3aNDU1VVZ7UsgFGbj0I0aMYDKZoaGSCNE4OjoCwNSpUxUVFel0+vPnz6W3h4KiYRAR8d80UTc33L8fjx1DEebKcLlcIq2koaFB9G9pNFpAQICIp3V2dlZUVJw2bRoArBE+/IrPw4cP1dXVSf1nNU/82dnZpHWWEsCnaCiUlpaGh4d7eHioqqoqKyuvXr2ay+UmJSVJv3OPHj0A4P79+9JvJVuEOupz5859+fKlj48PqdQFgK66uti6Nbq7Y0gIfrfSe+5cBEAFBZSp5tYvyOfPn7/r22/fvt3GxoY8ystEEIuiQXPt2jUiVbNkyRIGg2FiYvKtl56fny/M7pqZmZHmOBJel4vN1cDn80ePHg0A+vr6GRkZ1Rwp/FDu7u5WVlYMBkNBQWHZsmV1ZqpkXL9+nZRUKCkp+fj4cL+XrRERgUAwaNAgAJg8ebIMLUREamrGT4AMXHqi8SBudI0QFxdnaWl5+/ZtJycnoiFkYGBga2vr7u4eEBAQHh6ekJDA5/OlN5KCot6RmloxbenhQwwMFGspl8slt8DGjRsvXbqURqPRaLStW7eKuJzNZpNZXBLXbsXGxmpqapJ4XI2/ofHx8SSZINasJgoKuUPSR3PnzmWxWMbGxtJv6O7uDgCi/6rWAQKBYMmSJSQy+Oeff1Z+/dGjR4sXLw5xcPhPbKB5c5w/H+/eRYEAr13D3bvx61f8+2/U1MTQUDl+ip+ML1++EN+exFnIv06TJk0oXRIKAsnWKikpbdmyJS8vDxETExMvX768du3akSNHVlapIDCZzE6dOvXu3Vt0Rb26pKSkhHSXmJubVy7oq/5DkScfRUXFejtGJC8vj1zzAcDS0jI+Pl76PZOSktTU1ADgiixGekg5EZmiXiGtS8/n8xUVFWk0mjSVPI8fP1ZXVxfeuqqgrKzctWvX0aNHe3t7Hzx48O7du1RfLsVPwq1buG4dBgSg+JpbPB6PCGirqqp6eXmRX5/qC/BycnLOnz/v7u7esmVLGo2moKAg2YjsFy9eEFFrJycnEfWZKAF8iobI2bNnAWDYsGGkjkx6OSLSODp+/HiZmCc9pf9r797jer7//48/SgeHTiKdSLJpExYW5XBBc8glbFjMfCfNMGZhhq/fZTO++1x28RlG+1rfslyGXOKjRlNUY4gwKWINbZR0wESYQ4d379fvj9fm08fGqve7w8tu18v+6v1+Pl+P12U63N+v5/PxLCubOHGiiFhaWj7pezM7W/noI6VLl9+Cvaursm6dsnOncu2aMnmycv++0gQaaD+VCgoK1q5d6+rq6u3t3QQXd6ARqScaWlpa9urVy87O7pG/nFu1auXn5zdr1qzIyMj09PSmttj+j3755Rd1QV+XLl3mzp07ePDgP70pX1/ft99+OzIy8vjx4/fv31cPAGrTpk1OTk5j38GjduzYoW6RaNWq1dq1a434eHLNmjUi0qPH0Fu3DF35uGTJkpEjR7KC8ulgaKTPzc0VkQ4dOiiKUrd/r6dOnVLjQVBQUGlp6alTp7Zv3/7JJ5+EhIQMGDBAPQf4j5ycnAysHNA6nU6nrp9v2bLlggUL1L4pj/xdXlVVlZ6evnz5cj8/v+qtU62tret2mJZer1db5Y8fP75W/ZZXrlwpNMCHpqi/4BwcHNR/8wfVxr4GOH36tIh07tzZKOUpilJZWfnhhx8OHz48LS2ttr+CS0tL1T7qdnZ2qampNRqTkaH8938rK1b8+8CO8HDl5MlaVg3AUDqdrlu3bg9/p9vZ2fXv3z80NHTTpk3Z2dk1b1HZdPz4448PNwjU8KaqqqpefvllEfHw8DDi9nKj8PX1tbCw6N69u9E/bqiqqvqv/4q2t6/603OTaqWysvLIkSPGKAqNz9BIn5KSIiL+/v5VVVW9evWaMWNGrR6hnzlzpk2bNiIybty4x8WDsrKy7Ozs7du3r1ixYsaMGUOHDvXw8GiC24GAhqfX69VGd+phxYGBgWoP1evXr6sP5F2qnYRnZmbWv3//FStWZGRkGPKhbH5+/pw5c+pwmldISIiI/POftW6lCTQWBwcHEVFXxHz66acGzqbT6dTT2kpKSgyv7e7du4GBgeqREyLStm3bN954Y9euXTX5rK2oqEg9B8vFxSUrK6vW1w4NVdQ1Ph98oLBuDmgM9+7dCw8Pj4yMrPnxY03c7t27R4wYsWDBgt27d9fwpu7du9enTx8RGTBgQA2P9GsAD5uhqh0uje78eaV5c8XERElJqY/poUmGRvrly5eLSI8ePfbs2aM2dGnevPmcOXMKCgr+cuy5c2Xt23cQkTFjxtQ2HjSd71ugcen1erVJnoWFRWho6Icffujj41N9G4uHh8fs2bMTEhIavc92eXm5ekA3oBUBAQHy+wkRQUFBhk/Yv39/EUlOTjZwnmvXrvn4+IhI69atR44c2alTp4ff8s7OzrNnzz5w4MDjntRlZ2er7Zq6du2an59fl8tfvKjMnassX6589plBtwEAhikuLlZ32k+YMKGJrCE/cuSIGo7q7xKffKKIKO7uSp02UCr379+PjIw0dlFoTIZG+kOHDqkPMaytrUNCQsaMGaNmCQsLiw8/PHfp0mMH/vyz4uKi9OiRMmbMOPI5YAi9Xq8eU1+9T/LQoUPVB/KNXR2gYUuXLhWRN998U0Tc3d0Nn3D+/PlicKvI3NxctcNlp06dHq7qVM949/T0fJjt27Rpoz63r/6h+dGjR9XFcX5+fkZZLAAAjSs7O1vtwrtUPRu4sa1du1ZEpk2bVn+XqKxUXnxREVFCQ+syvKSk5LvvvjN2UWhMRuh4n5ycrB6vIiI2NjYzZsyYMGFC+/a+lpaKubnyxhvKH3eR5Ocr7u6KiPLSS0qT79kBaMPrr7/esWPHN998Mykpqen3wgE0ISEhQUQGDx6sHtn45DOWaiImJkZdm1bnGcpPnOjk7i4iL7744p/Wo2Z79TAalb29vZrto6Oj1YX6Y8eO5acEgKdGcnKymZmZiYnJxiZwmubkyZNFJCIiol6vkpWlmJsrZmaKMY5YheaZKIoixpCWlvbxxx9/++23IjJ8+L4OHfzu3Gm5c6fodGJmJpMny/Ll0rGjiEhBgQwaJHl50r+/JCeLlZVRrg8AgJH98ssvjo6O1tbWPXv2PHTo0J49e0aOHFm3qf7xj39069atW7duzz77rIODw5UrV6p3rKypfftk3LhET89IR8et27ZZPfE36JkzZ+Li4mJjY8+fP69+xcTERFGU0NDQNWvWPO6UGQDQoqioqOnTp5ubmyclJb300ks1HJWXlxcTE9OrV68RI0YY66dily5dfv7555MnT/bs2dMoEz5OeLg4O4uvrzg7S06OVFuk9ZuKCt3Vq8UFBQWFhYXFxcWXL18uLCzU6XSff/65uv0KTw2jRXrV999//+mn6w4ciL51y6RlS3ntNTExkS1bpKJCTp+W7t2luFgGDZILF8TPT1JSxNraiBcHAMDI3NzcCgoKQkJCvvrqq2XLln300Ue1naGqqmrOnDkRERFWVlbbt2+fOHGi+jQpMDAwKCgoICDA3Ny8RhNt3ixvvSWVlTJlikRFSQ1HiWRnZ8fFxUVGRt66dWv8+PFbtmyp7S0AQNP3/vvvr1692sbG5siRI9VPBHhIp9Pl5ORkZmaePXv2xx9/PH78+PXr19WXpk2bFhUVZXgNt27Jyy9/aWqa8e2362r6s90AUVGye7fs2CHz5sno0XLihBQXy+XLUlQkRUXSqtXZixe9Hhliamrq7++/d+/e+q4NDcnIkV6VmSkffyy7domiSPPmMnGitG0rCxaIs7McPiyLFklZmezfL61bG/3KAAAY06uvvvr111/Pnj07PDw8MDAwMTGxVsPLy8vfeOON2NhYS0vLOXPmfPHFF2VlZfb29jdv3lTf4ODgMHbs2P83aVLHAQPEzOyxE4WFyfz5oigSGipr18rvjTMAACq9Xh8UFLRjx45OnTodO3bM0dHxzp07p0+fzvpddnZ2RUVF9SGOjo62trY///yziERHR6tr5g2xd68MHy5+fnL0qIEz1UhUlJSVSatWkpUlpaUSHf0fr9rZ/Wpl1dXNzc3FxcXV1dXNze3evXvLli2zsLC4dOnS404KhxY9/q8HA/TuLfHx8sMPsnKlxMTI5s0yZ44sWSIbN0pcnKSkiE5HngcAaICPj8/XX3999+5dEUlPT6/V2NLS0jFjxqSlpbVu3To4OHjNmjV6vX7OnDlhYWHnzp2LjY3dvn37uXPn1q9f//l338nNmzJqlAQFyYgRYmHxHxP9618yb540ayb/+78ya5YR7w4AnhqmpqbR0dGFhYXp6ekeHh7Ozs65ubnVH16amJg888wzPXv27Nmzp7e3t7e3t7Ozs4isX79+5syZISEhzs7O/v7+htSg/pbo08ewO6mNIUNk/XopLZXAQHF0lPbtpX17cXWVDh3Eycm6WbOCR96fkZGxa9euiIiIOiw6Q5NVL0/pq8vO/neGt7eXw4clLKxeLwgAgNHs37//pZde6tu3b25u7vXr1/Pz89UDk/7SpUuXRo4cef78eXd398DAwC+++MLExGTp0qXLli2r/rYffvghLTFx1pYtcvbsb1+yt5eXX5YlS+TIEbl8WVq2lHfflTfflKAgeeUVY98fADxVSkpKOnfufOfOHRExNzd/9tlne/fu3bt3by8vr549e6pHfvzR/Pnz165da29vf/ToUc8/7kqvsVdekW++kS1bxODn/TUSFSV+fmJvLwEBcvp0jYYcOnRo0KBBDg4O+fn5LVq0qOcC0UDqPdKr/vlPCQ6W5ctFp5Mvv2yACwIAYAS3b9+2t7c3NzffuHGji4tL3759LS0t/3JUdnb2yJEjCwsLvby8unfvvm3bNjMzs4iIiGnTpj12TG6uJCRIbKwcOSIi8vXXkp8v8+fL3r1SVCRTpxrtlgDgqVZaWvrVV1/169evd+/eNdzQrtfrx48fHx8f7+HhcezYsXbt2tXt0q6uUlwsOTnSpUvdJqid1FQ5cEACA8XLS1q2rOkoX1/f48ePr1+/fvr06fVZHRpOgza8XbhQzp1ryAsCAGAQW1vbNm3aVFRUbNiwoaysrCZt6vfv3z9gwIDCwsJBgwa5uLhs27bNyspq165dT8rzIuLhIXPnSlqanDsn4eHy4IH06iUi0ru3/N6yHgDwl1q3bv3ee+/5+vrWvEGdqalpTEyMuiBr/PjxZWVldbjulStSXCy2tvLss3UYXRd79sjy5ZKYWIs8LyLz5s0TkdWrV+v1+vqqDA2rgSL94sXi5CQeHpKW1jAXBADAOGbMmGFqarpv374RI0a0a9duypQpCQkJj/RYeiguLi4wMPD27dujRo26ffv23r17nZycUlNTa3H63XPPyaxZ0r27fP+9iMjRo/LCC0a6FQDAn2vRosXOnTvd3NzS0tKCg4PrsJBZp5N33pEpUxqugemJEyIiPj61GxUUFOTh4ZGTk5OUlFQfVaHhNXtkUx8AAKjO399/0KBBzz333IMHD3766aczZ85s3bp13bp1Z8+eNTMzc3d3N/u9U/3JkyeHDRum0+neeuutjIyMs2fPdu7cef/+/V5ejx4j9NccHaWgQHbtkvJyCQ4WjpEHgHpmbW09YsSImJiYzMzMqqqqWrXKCwuTggL54ANJT5fBg+utxGr0epk7V8rLZfXq2p0LbmpqqtfrU1JSrly5EhwcXG8FouE00F56AACeArm5uQkJCbGxsUfUHe8iLVu29Pf3DwoKGj9+fKtWrRYvXqzT6aKjo69fv96nT5/ExEQHB4fGrRkAUHMpKSmjRo3S6XQREREzZ878y/ffuSN5ebJzp/zyiyxdKp98Ip9/3gBlytmz4uUlbm6Sn1/rsb/++qubm9utW7fS09N9avuUH00PkR4AgFr76aef4uLi4uLiTp06pX7FxsZm1KhRZmZmcXFx9+/fHz169LZt21rWaoMjAKAJ2LBhw1tvvWVubr5nz56hQ4c+8mpRUWVWlnlWlmRlyalTkpsrVlYyf75MnSphYaLXN1Ck37RJpk6V8eMlLq4uwxcuXLhq1arJkydv2bLF2KWhoRHpAQCou/z8/Pj4+NjY2KNHjyqKYmJioijK9OnTw8PDHy7IBwBoy6JFi1auXGljY3P48OG2bdtm/i4jI+O552YePLjs4TstLaVbN/H3lw8+kNhY2bFDdu9uiAr/538S9+ypmjhx0Pz5dnUYXlRU1KlTJxG5cOFCDQ9nRZNFpAcAwAjy8vKioqISEhJ8fHw2bNjQ2OUAAOpOp9PZ2Ng8ePBA3Xle/aVBg15rxVzNSAAACgZJREFU1myrt7eo/z3/vJiZSVqa+PiIubnExsqDBzJlSr23QOnbt296evr+/fuHDBlStxkmT54cExPz/vvvr1y50ri1oYER6QEAAADg31JTUwcPHmxmZmZiYlJZWdmvX7+BAwd27dq1d+/eXbt2NXl8U/sxYyQhQcLCJDS0HsurqKiwsbGprKwsLS21sbGp2ySnT5/29va2sbG5fPmyra2tcStEQ6KDLgAAAAD829atW0UkJCSksrKydevWBw4cWLFixZQpU7y8vJ6Q50VEbai3ZIlcvFiP5Z0+fbq8vNzT07POeV5EXnjhBU9Pzzt37ixcuNCItaHhEekBAAAA4DeVlZVxcXEioi5nnjBhgoWFRQ3HBgbK66/L/fsyfbrU32Lo9PR0EenTp48hkyQmJubl5Zmamnbv3t1IdaFxEOkBAAAA4DcpKSk3btx4/vnnv/vuOxGZNGlSrYZ//rk4OsqBAxIVVT/1iZw4cUJEDDl/bvPmzePGjauoqBg7duyMGTOMVxoaAZEeAAAAAH6zfds2ERk4cGBeXp6Li8vAgQNrNbxNm9/Osdu48WZRUZHRy0tISEhOTm7evPm+ffuuX79ehxnCwsKmTp1aWVm5ePHi2NhYS0tLoxeJhkSkBwAAAAAREbl/f+PBg+cGDuxmYWFmajpp0iTT2jevnzBBQkP3ff+949tvv23E0q5evfrqq6+OGTPm2rVr5eXl8fHxzz///Lp163Q6XQ1nUBRl0aJF8+bNE5HVq1evWLHiya0BoAl0vAcAAAAAERHZulVef118fSUvr0rkRnJyO2/vOkxz9epVLy+vmzdvbtmyZfLkyYbXFRsbO3v27JKSEhsbm6lTp2ZmZtra2u7Zs0dEPD09V61aNWrUqCfPUFFRERISEhMTY2FhsWnTptdee83wqtAU8JQeAAAAAEREZOtWEZGePeXatWZ2dnXL8yLi5OS0evVqEZk7d+61a9cMqejKlStjx46dMGFCSUlJQEDADz/8MHDgwNLS0t27d+/du7dr1645OTmjR48eNmxYdnb24ya5d+/eK6+8EhMTY2VllZCQQJ5/mhDpAQAAAECktFRSUqRZM7lxQ0Sklo3xHjF16tSAgIAbN268++67dZtBUZTNmzd7eXnFx8fb2dlFRkYmJSW5ubkVFRVVVlaKyNChQ7OystauXWtnZ7dv375evXrNnDmzpKTkkXlu3rw5bNiwpKQkR0fH1NTU4cOHG3JfaGqI9AAAAAAgEhsrFRUyZIikpIgYGulFJDIy0tra+ptvvnnmmWdCQ0Ojo6PPnz9fw43PeXl5w4cPDw4OLi0tHTVqVHZ29sPW9FeuXKmoqFDnMTc3nzt37sWLF0NDQxVFWb9+vaenZ1hY2MMN9pcuXerXr9+xY8c6dep0+PDhXr16GXhTaGrYSw8AAAAAIkOGyMGD8s478sUX8uKLcuKE4VOuWrVq8eLFer3+4Vesra179OjR+3deXl6PDFEU5csvv1ywYMHdu3fbtWu3cuXKKVOmVH/DtGnTdu/enZWV5eTkVP3r58+ff++995KSkkTE09Pzs88+69ixY0BAQGFhYbdu3ZKTk11dXQ2/IzQ1RHoAAAAAEElNla1b5dYt+de/ZNUqWbDAKLMWFRXl5uaePHkyMzMzMzPz7Nmz1V91dnZ+GO99fX1LS0tnzpx58OBBEQkKCgoPD2/btu0jE06aNGnHjh0HDx708/P74+Xi4+MXLlx44cIFEWnWrFlVVZW/v//OnTttbGyMcjtoaoj0AAAAAP72rl+X//s/0etl5Ei5dk369JH/fAZuLFeuXElPTz/xu9LS0uqvmpqa6vV6V1fXiIiIxzWxHzt2bHx8fERExMyZM//0DZWVleHh4YsWLaqoqPDx8Tl06FDz5s2NfydoGoj0AAAAAP723n1Xli8XOzsJDpZNm6T2x9HXTXFxsfr0/siRI6mpqZWVlUOHDo2Li7O1tX3ckICAgJSUlHfeeWfdunVPmDk3N/fBgweenp5mZmb1UDiaCv7vAgAAAPjbKy8Xe3sRkQ4d5OZN+cNy93ri4uLi4uIyevRoESkrK7t3716bNm2ePKSsrExErl69+uS3eXh4GKtINGVEegAAAAB/exYWcvu22NhIcfFv2b7BNW/evCYr5NVIf+vWrfqvCBpApAcAAADwt/fBB7J6tSiKzJrVYKvu6+bXX39t27btH8+fx98Te+kBAAAAQBuqqqouXLhgaWnZsmVLa2vrFi1aNHZFaGRN+vMnAAAAAMBDV69ejYuLc3d3P3r06OHDhxu7HDQ+Ij0AAAAAaEZOTk5iYmJGRkZjF4ImgUgPAAAAAJpha2vbvn37v2yMj78J2uMBAAAAgGY4OTl5e3tfunSpsQtBk0B7PAAAAADQBp1Od/PmzXbt2t25c8fU1NTKyqqxK0IjI9IDAAAAAKBJ7KUHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJhHpAQAAAADQJCI9AAAAAACaRKQHAAAAAECTiPQAAAAAAGgSkR4AAAAAAE0i0gMAAAAAoElEegAAAAAANIlIDwAAAACAJv1/ZKkM2NHnb+oAAARfelRYdHJka2l0UEtMIHJka2l0IDIwMjMuMDkuNgAAeJyN03tMW1UYAPBzz20vj1JaXuM14EJpuVBaaKnlsbb0AGVjTJdMxKHpdlkyvSobYfExhAWyDJEusIFxRHCDRQ3zlf2BCwaWrScRnQku08zMP4Yj6pxzyYhLMJsuwd6vteiQzJvcfL+ec77vvG4Xz338PQo8GrTybAy8WwJvN8MhMRAZlnMLgcgqVkdJjizzD4R6IBPjCAQdOFAq2BEZjJjjbfIAJjwy2MAwgREwKbNSAjLY/64QSFh7jlCFSMTLMTzTA/Eh3auHh6o/9BAeXLk2lBlu/x/HKEccqoRXMmDJiigIzL/DSpFVxwFJ4UsNX+6q2dcYuGaC1v33kYQaIoINrAoxiMESxizPKiSsUIpKTsJcBIqIRJFRfFS0gFVRCqyKQTFqpI5VMGoN0miRNo6PixdwXAJKSFQwiUl80joBJyWj5BSUkopS01BaOkpfj9ZnoIxMlJmFsnjEZ6PsHJSj43W5Es7Vi3qDhA15fJ7AC/kCFgr4AiNvLJRwoUk0mSVsLuKLigXWbBEtVmQtQVYbsj6CLHYJF5TypWUCLi1H5RWofAMyOESHU8IJLuSqRC43chHEVYlV1YFN1Yg1HtFTK+FaRqxmRa0KOXVieR6yG8VELrB/JYNrPLWcQslVVbPKGJVWo+Zy9QaHU6cU8spLC7hCk9liNybvYAPHGv73sdfa6bb+Q375h/PS61TT1gH+9rkBau8zgHdn9lHf7Ph52Xs3v0qH778BHphspY8drHLJVmkO0LLLU5Wyjfv30X6t1S37Ys9TlHupDHz79lY682gv+IXNXjplPg0evF9K73b5wHabkcbyk+BXFhPofu8EeGc3QzPSPgc3NcbTzt/2gitPCPRIdT24r8tM9c1XYQ0LxzyUV50CH6jLoLtyTGCDlaP2Gz5wyd2bfvJLjlP2hekl/6exZ2BfPV/O+DPPXId9tXjf8jfNpkB7p7fRv2G3+5zs+p44/9tXj0P79FjD+UL142CTosPla1wEu9K/q+xBM+BL14vdTomH8zx28jV3hZMDn1a/77bp68BexxW35Q8z+OVT99zGH9rAQ7cSSP7Uk+AT2XoyuLUfvFTmINqKbrA7rYromnXgYUsT4e1R4Lr2ZlLS4oA1zGU0kA9GMez98ovt5FZ0PbRL+kOkbGQW9qvjjpLU0WIw3zZMnl3+CMbv+nOI1I8VwTmLz4yQLUkdYO/XJ8mE5x3wRFovWdjkBP/67kHisB0Gb9/RSSI+mwp+A0wr8U3vA89f6CdW1XFww84ucva9H2GuN5e7SDKvgfWbqg+Ta65icLKmhcQM1ICf39NB7PpecOoTT5OsyeA3bPupmGy744d9zc+lku2blsBfFOjoUP5GcOTPKpo5NgTuba2gF/O/gTuNj8mnzfOxUOeTbh/1qD6E9eTOHaHLX/FnZV9xjNCbN+wwJvrOKF343Qd2mAbpeO44eN1fD+NJ/YYqvyEAAAYBelRYdE1PTCByZGtpdCAyMDIzLjA5LjYAAHicfVhJjiQ3DLz3K/IDnRA3UTr4MIs9MIzpAeyx/+C7/w9HKKuVNTDhWohqIUoKcQmy+uXg4/fPv/39z7Ef+vnl5Tja/7zmnMdf1lp7+Xrww/Hx5y+/vh2fvn/4+L7y6dufb9//OFKO7PgOnj9iP3z/9vV9RY5Px6vE2aVPz+PVT3HVkKOdbT3uL+uC5ilNeo7j1c6pI3nEf6D2gA5C+/GqZ28qGgXU33e1OVzx8dQAeBTQeOfqJrgcoBJdvCLQF9TPOcSMABlzRiuQ+dh0SJfUQ043+MIL5HggJdxsHHrmUO3VnvN4A9LOHtl7AjmGiluBlLY21RPshunhp+pQmxVUjm9r12Gt93HE6ekeWkGvUOEuc+Ja2NV0iFZBlStU7czWFAkQZ0yQLrmuUM1TPSYAcaohvNX9JUh1nG3acATjlNn6qEIq/doTUWxDDjvTzEd5/VxObWfY6OaASgaLoYCOx51wqlgipqlImCr7ZC6n6mk+zQJQEx2l/7VdVOF+d7oSmY1IVEgh1XHaSEXU5WwjZJQltQKVOBRJtfxjM7MKqRqJJg6dLVbuu3hICV1xQkb3gVsB4D46QlsgV5z8zKk9/HiFcyduVTJdgTL6yaMTKiPHKK+/SkpPTUukByvaTGflfl2RavRpdF2f1F2rnFJGqjH5UNHcdPQ5SpmgAsLpSDizy1O9zagy2qh+qOPuDaRBOVq3XvnUGCgUZ0uII5Ftpo/Kp8aCQm0MnbgHPAY9HbMUScapIze7IA2xZxp4VM43xmmp6UARYs/M6VolqTFMq0QbcoPxyhZRqZkxSqgRw53aYBa4rLouoIwS1AT5jooitPE7pe8noQo1F2/rUhE6WkUAwvAG+TkjEZ/lfUjkLG/ljBPUvE0VyCDSJA09onIqWsgf3FUjNXlsoF2VVFGZOJ96Jw3VxU2niZU9ipGSjr2GrtZgIxCACskeJVC+pnMFHT3Fe5Un3i/kNJxOjbKABpSOygtpvFFnvjp8W+65AoXwQxeRnGzQ6JTljdiiFOWkSGj2EvhAyxqJdt19DKRf4PQZGVki5UIaihkahKbDJKyChKb1C12fac3YSdIVwlYhjUj0ccuZqz9AqsoYIcmBTJSTtUycPhuUrPJSrBjFOZF2SXnOHF7KU/QHEu6cY2madTSoCpooURzvLeGnVc1oVFaeP65sBqCPVc4tRbISvZgXgYSIsJ45HkBKq8zrK05wUBua/Rrk+ujVtbpc9QxlSFlaGi2t9EDXpScdsxuCDyTmCS2p9vdBQibEaTWThExlVXp9daiBsFu/OgR6mZcdssdjlOmhzFRs69K8TOreHxQC3czzihdGtNJd+Zg72e9Xk8acqDWF8YBC8tfgSwgGy5LChPy8sgRx9bZSZgRbdjV5XrPfxJRmMowhM9SBVRWTa6SACIhj22R7bxhDsuL789vnH0b8a+j/+O3t8z3086n3YO942z28o9AOvwd0PuMewvHH0e9B2/HOe5rGj45j3COz4D3vuVj4fp5+lUbkacgVjq+ci3/58tPmxKVl7hXSpNlE0RplmVjfvfeMx/omjalTltm8hcRpNnUMkUojm72QPpefR0KhQdP+4URd7sXrdrFiN5rNX8mfK5u/kj+Mbl8rmcPoZq5kDqObuZI5jG7mSq/D6GauZA5jzzOSHGsCepqFhMY2Z6PPYezOjJUahsnlaYYRGtucOUTT2OZszBEY25whi05jmzNGDKexzdnIGcafBwah8c0Zc4HS+Obs5MyVzZm1TeN3Qq+MxmtzdiY1zebs5EyzOTv9DOObM7qt0fjmjK7qxMRz9xSa2JyDfoaJzTn0sbI5hz1WNudYNcje9dTF1r1ic0a3UprYnIOcubI5B3MDJjbnmNdKf+4kTs/3zZk/IWn65tz1yvy+OXdyhumbc/fHyubc47Fyywf9nFDjJ112mr4p95UaUNYnjXWafFZSp8nNOMk4nzQuKXLcai/Yo+JvCH2Mi92Zm3H55s7BJF+4/s4mqu2ztvLv93/G4PPLv1CnTyTqo8nBAAADEXpUWHRTTUlMRVMgcmRraXQgMjAyMy4wOS42AAB4nE1Uu44kNwz8FQNOpoEegS+R1A0MHNDGwdFsYDgyHHV+iYNL7uNdpDbwYnd2urpEUVVFXTffj+ct930/rvf1+O3j+Pv6+vWPf/Ta/x8fx/XW/714bNb1cV39h89ba/nb8OLP4/24Z1f79X3g8/HtwO+3457HXmeP67iO+1Y8X8fngxz/3t/5l5+PJ8/h7Ot82mATmefryTGYOM6njiUZuaFsSIaTsHyydOX55CHTSQtCLVMpiKdz17KxkvWkwbnW3KRkl5OHKXbeCE/TU0akSCM6fEYAyRS2QmRgeZ42RFJsc1IpzjkszLoySq4l4Kjk7pFGEAk4c4VX5TXE5nIgotS756ClaacPXgQhisPmlKeOULW160xNB8Lh+xlcxhlCOHj3B6oCUZa0roL2zHByD1faW2kGmudBObl0DdB5TpBIV4RoYx6LZqlobLOqQzJPLZpZugUgG7HEwUJrCzsUTasH88IYvmXtKUNC0XTZpCp1GqrOJiyob2IGpahkYdWipa9EHzw8cyNQo6yDP24wGjUnuaI63KAQKYRWlFTQNQUeoBfECYBDIuemhBq8eHWUkosSseD7q02hRjQIcrwgsKJ0pdK4/HrBXMjYOSUKQmVIzopwVDtToCYSMaOOjxemq84A8hKug4YiuN4kmVGBnAh51rJKx+YsZa3dHS+zOJpzlT6MlFCnGP55NLCUKsU6Ed4GNKQiizTCDF5Dzxogxdi8BAJLVBCZesiwRWJzpHrGjH5WmFLZhRLaXQXaxKeJZx8FMa1MwvbaLyCsMhYsCpdesCq10Cly7mf8lBqirtpLjDDOZcWsyFdRfOtJp+BqtLaFcSWzSMcFggVBjL4gPKOtgElRyZgUVv7BYQQVwMJox54QXrFaVbjcl0jiLOodReTaco+sT+kkGpPF/Bw2n7Yb/7wPoseq1FaXzwsJgYtaSIQbxqLvBCQJm9XSnLE2grHs86i1cE84BTq6nVhqqHGcP/76gmNC9ipAP37/gr6Yfv4HH8MoSbAfT1MAAAP2elRYdHJka2l0UEtMMSByZGtpdCAyMDIzLjA5LjYAAHicldN/TBtVHADwd6/ttaWUll9tYYVef9Ae0NJRSoWWwr1olsw6JWSZydiy8w/DGbdoWAdOVLahZGUjcWRZNp1ZIn/oMpnZ/E0E7v0xNjUxsC1GnUxNdPjHnKiTbfhj9r78ClRNvOTy/bx3733fe9+7+2l06CpKXya0fDWl7/vS9x6GRWI6Miot4tNRpWIFiGpWkKDN/BMWhsBUjLUIHuClB1phZa6MAf+eUze/DbyYe3XUIU6JmY9XTl9eMWP+Qv7/ccyl465eY+GUkFqVsdR/7Gn1WVe1M7ayXNCMV6CUQ62HwKwMS4PN0GYX3/TiMovRgBjEYA6rJKxSi2qNhDWsyGqRVod0ek6fJeEsg2jI5rKNnDFHwjkm0WSWsDmXy81DefkovwAVFKJCC7JYkdWGbEU8thVzxWvQGjuyl6CSUq7UIWEHJ3JOCTtdosstYbeH85Tx2OPlvD7k4xFfjsorUEUljyv8nD/ABap47A1ywbU8NleL1SEJG2okXBMWw7USro2gyD0oUoci9YiNSpiJoVgDisV1ONaow1Es1urFkFF0l4oBHwq6uAJWOT2Oshq1is0y1IRr9WyOyVwdMrIOzulyl2o8blfQy/LlFf6Az/oXThdp6T951HyYXhG+V/4XlHNqP91ddBI8uqObftJRIChm+X00lWwHn25rpw8Ej4HxK610Z1kf+O3hTZTfEAHrZppoZ70NPGyM0W9uSJDTeK+HRoVvGxWPiQ76xKmeMcX5R3R08uIY+NxbKuqbjsuKQxe/kD+fawUfMU7IiT+Og+NPvSp72DfAzEPPyiW5B8HNHpf8W/HL4CHNO2NTiQ7wni99DeeDfeDXB15s6t61EaxJuoW1qXbwc4WPC64TIbCNGxQunW0GX54ZF85dMIPr/CNCkT8B+wz03hQ6vqqC/l2V2WTiuynod866SBFrhP7uq2FiqXsJ+v0nE6Rz22fgpw80k67Aehjj3dRGKkkb+PnG3aTkYR84siVJRs4OwPi+Q1vJe5YAuGFoK1n/QwvU81bL/eQGcwWsTSWJaXYQ/KvxGfJBWQDqf2Y8RX4+LYB9qX4yeb0ffO3AC6R/5Ax4y4Mpcq3l63kf3Ul+uf0a+Mn87SR0PglGhzeSufgj4Nvr9pLh2b1xxTPr+snvUQls375f7vJE52vCvCt/bLaDH2v9Ub40d3cUas7dkgfudMI+39ycRys974P/bLDSy6Yo5E847PQj9iC4uOuunH1sA3jHh1X0eK8XnDco0fD4dZg7Xd9L3WYr5Jcv9NB9U59CrbbdTNLNPU743k7cOUQnJqdhjOVvaU8uGswnk5YAAAVaelRYdE1PTDEgcmRraXQgMjAyMy4wOS42AAB4nH1XW24cNxD81yl4AQ/Yb/LTkuwgCCwBsZM75D/3R6o5K844aWRX29gd1HCqX9Wtp5av319/++vvtl/8+vTUWv+fvzln+1N670/fWn5pz19++fWtvfz4/Pxx5eX9j7cf35tL84F78P4Z+/nH+7ePK9Re2icah/XRuzY6YlJXbv3o63Xdy+0NyDj6EGMBUpj67AVS1pl2TFee0vgQmmEVUhfSD/ZpKk2OGEPA+79Ie5xJ1oW4KZDRuUL6QuIotiGJFIoelUfxQCp3GwyeQ4faLJBjIemY06lTehTDehTIuaJEh88+jACYfY5pBZL6OrTDeZfI6KjMqZVLRHnqRECJuOOmo/Nwr5giNjgV+RScOhOqczhTBZU8dRwsJANQxvnBUhJYifJj9PSrfULMJGiUbtkJtU5ugCoiYDFHBV2psqNHUIz8JmF1odDKlRwTR2nkqRLRRxmB0d7TGRNR94QOja5lBOZKwaGkLJ7nowJkVAQ4s4XL1FltPZ+GSNkn2VJ0EIPoOhPV72X9MYMpiJpMW8gY08ua5mwpOTzw1EjnAmVV9glnphDSGe4rUex9UpUozkSh9cgHsoMz2ZlNK6SDJ9qZyBXIjqeL1xoRKKk4XEliVVQIc/cKmS01jvAOfpmDEDEteWaS0CYot5SbJNpFZ9V8+HtHsxzSFeKUp3JHQkvoyhII8hh9QT0U6lNBs6UgE3hqR0sjpqT4WhWUZJ5ID7VhuA1QJ/hVJUoyURA0iwhkEm45qc8qqmInlCFpaFpwBbKXZSp+ckUd9yTQ4ZYTl27FA2qKngJgsAwrY5UdhVi5GpvljIDwzyqrkvKXTIeo5TTh0bNfC+Xvp09hCKWjC6KbcolcmYqDUUnobcke9CiRfCJ9Yt7gJ0TKyCrfdeXJgRRB+SELGD+lmqim73GQCzuhXyaKpFReFO/pOyONgTNZJ0mJ9LNKApHHXUgBbql5Rj4dTTyyjoA0mtpL5Dg9Iptuq4hc6lmm8/TIgTxbw9zLdrI1oRTCkAN8DYgOLS2hdEq5j/BYaoJ4oloq6FokAqIbmq2PuTdzrlbQtUkEmpR9+TJ9PLz7N3INqInGCEJpUT7eeyVmZicStSyQk/QpoIEV0tvXRGImuWY5YTqNspXQNV+T54TWIDzIF3HOngI5Egk1m1ieeu4c3jEeSug5nBSDbA6MdUJTz5hVSr2372uPihgckjFFIIyrSDk9di4NyQUqlY9QXVWqPAdUbl1oEl8ZIshVGQJI2PtaJOE4GmSl1Tiikr4vb68/7aDnVvr8/vZ6baX55mv1xI8m136p+bmWyHzbtSniR/NrHVR84tr5CJ9xLXaEz7y2N8rPfUfTNES3VUzT0CaXqVlm84NG0zKbIiVDGNosKWnC0CZKyRSGNldKsmk2XUq+aTZjSsowfN9UKA1vzryCib8roCuiKL/NOTf6NLw5Y5OgNLw5o515mc0ZmwGtK5szJ2cY3pw5OcPw5szJGUbug5vamsq3+axpZHPOXT6NXHWwCgGyuDlLxhlGNmfJOMPI5izJGUY2Z6ggp5HNWZJzXtmcJTnD6H2CURrdnDXjDKObs2acYXRzxuTJn5grtwlD68rmjJlAaXRzxsTgNLo5azz4bM6YAJxGN2covWb92F3RNY1tzrbqmaHKN33WNLY5W8YZxjbnnO1p7Oq65Axjm7P548rmbPG4sjlDDhXtaJuyrdKAoN2kjdL4ZuwnAOJ0k6nzymbsi/G8JMOXROittX3pBN2a1LMBszCuRyXhDPt1VxYGgnwVWIrZXbry98c/4/j+9A9cqfBRw3/s1AAAAp16VFh0U01JTEVTMSByZGtpdCAyMDIzLjA5LjYAAHicPVRJjttADPxKjjYgN7gvMOYkYI4zhzxB93lBHp9iK4hhy3aJTRaLRZ1fj4svvB7n1yU/1+Pr0vn3fZ7nfB4f388v/LxsgvyyG3mc9v+m4Xo6Ltelz5/rkvPx+cT783nx8/fjfE7Yx/evP48X13Kqg1c2kx3vF+eiUgeiwtSD+OowPWQpd/ogsSTaD11ZpXHHsJMeBiRJBsFN8QKinJQ3YkKBPGVl+xSv7uDJnOV8I9GoSgvX6l2MUCwMkGk3eLxfDULMcrx4kVRMcrShEwXIukI2JMrax0sQnjIHYxVF8/ECFU3uDTlxxPEylATf98sXZXId+KHpu2Fd7Qk2CNLMrYqgoFoOVJa3dMvYxCY7OlTkokVMYvdBLgUHXixtNUGQc7gjk2vHIFk91HVF4uYwT+gAqcCpE8GIkSB08MYIOHaIhIhPSDMHShEOaRjy5Apj8Jg8KoJTtTIIioNrqjqOQV4e7ScR6QCYPEFmhAhhUEBwXKoGibRE9xglqW0d2XRCbJmXDxCsO40vz9y1gy16IwJLTBoAMN2kqZGCkDdY/iEOiFaJlucuHuY6DmW+k9z2lKK+66SDAjiSjztgYBGBNyF91P4fLfChqTOkhHmjMQnwQ3zvAA4AsRp2zruGYLT4sua7u3QAICcWsWnCdwzAuY13UvZO8KZQKHGnDcfsAHlEjwJwgYTKdiRN52O2qIwtk6Hh8W3CRjniwvG992OwFGzB7EVU7LCGigl2PAdjA+aChiY5ojcC/2IpxrVeuvN0YddAXHha3OvVMisYhO0ewODsnkdCYZPubccgdfigou/MwCy3R4Ix8PsJoLQVYMxd9kFs4Ex+GnFJxD///AVCH/5MaulV0QAABRR6VFh0cmRraXRQS0wyIHJka2l0IDIwMjMuMDkuNgAAeJyV1A1ME2cYAODvPtrSg0IroIAIXPktUn6soBWx9x0Fofy0tiJs/sTTLNmNuBgFNgKoOIFl0zAncw6zJWwBTCa6LWK2aZReozEo8w+N8adsom4iuombSyZq1r6FsaUjbpdc3ufe3Pfe+71f01+OHxhErkuJJq9S173SdTdQvkhwRR8Ky5DGDYncE13PvCviychkuCMlGV+BKa8VE294L41zR6+8r2ehzws//YI42RHxFJIRSPhQ/4aJV8ajVy88zMP7W//sceopTLWZF8/Pu/upEohxRQkNgRoPSgjSiWpy+C5FTcSpR+HL62HLXucw1fn8bQfjw/ovc5/6JP7HIgpaQJQ/ohgKazDl46qApBIJlsqQzJfxlWsw7SvBtB/y80f+CqQIQAGBKFCJlCpGNU2DVUEoKBgFh6CQ6Wj6DDQjFIWGobBwJnymgGdG8BGzBDwrko+MEnBUNIpmEKNG6hgUE4ti41BcvAbHJTAJiUyiRoOZJCZptgYrk1GyFmlTJDgllUlN0+DUdCZ9joDn6HjdXAHPzeAzMlHmPJQ5H2XqkX6BHOuzkH4hysgWcPYifpFBwAaW1xJEOA0mOYzUiIy5GmzMY/IWo8X5KL9AwAUm3lQo4MIivqhYwItLUIlZwGYLb1ki4CVW3moTcA6NivN5Wwmfo0BR4XyiGrHpfFIkY9DxiqUSKoR2/zb86ByFvzwgUJmsJTkK2cyIWZFR4dLoqMgkRhYTG5eQqJbOSWcNOtncjOxFrlBgKiwqzpeZLUustpLQJz6uwf/1D9Jx2sr1fzBicD+cd+ZxRyrLe91+9NDKUZfkdrevOVO49y6/BHnZqJq730hDXjVKc2ucHtfIn5Jh7SrwDnk/Wd3ksWnoEClu8QPfTu0hlRUfgVnnKULrToBjLR1EveE2+Ji5mtTs6QcPjcURxc0fwWulLezWaU7wsvpWQ8jdHrClb0/vjYvHwIcXXOk9yoyAuxr87KfWt4GvXhLsO5d/Bl73Tav98c914KYhh9041AxuTB+yr3ye66l5QC4+GCgDm0vDREdAIHjFcJpYeisaLFyZL6q21IKr1llE62xPnarg1eKbLUbwq/HlYvWdCzC3FTtZ0ZZ1ELysMVts76Vg5iXzbeLCrbms2191l4lbq5TE7cS6XLFBZQZvf1ggvqz7EFy7Z7VYGdgFHlS8Iu7QXwZL+zaIlfZmcPvS18UebRo4uWmLWKG+CfXru9eK8U86j7t9orZWvP5IDq7PCmPrRpqg58eO++x5Yzn4Ymgfm/39KPQcYkshm8yvge/+vo10hvaA36qPIMVYBfWzratIiX0APJKRRj4ONkAPUQ/MpLt8BzjaqiKOI1+Cl9LppF0+CG4WLYTdL+XcftvWRJq1ZyGfcv1z9tvbD8BHTnQeG7A/A3/9E2Z/O3kS3Fp0jy1uC4C1h3/NZ/84tw/yF+J3GeZJqsEbxU9Zf4MWnLvmHfYcvRf63L+8lNwZqoD9XtE3EV1rBHj4jUbyw95PwM6jYySrbQXsd/ToGaIcWwl2nKa570y34By59WOkzT4INas7aO7SFznwrYoONVcoN4LZ91O44eTt4MjNam7jvh7PWW+mOX2dx10xY6Rq4F1w2foz5KDSUzPIdIjce85BPsG0jVR3efxssIxcPXAD3nHs3kbOPr8G/RzcfYjUOJzgTbNriGzXU9jLjD8BL0upfJy5F7MAAAbwelRYdE1PTDIgcmRraXQgMjAyMy4wOS42AAB4nH1YSXIkNxK88xX5AaYhFkQAhzn0os3GRI5pWvqD7vq/yR1ZRFabhVTFii6CngFH7OiXg6/fvv73z7+O/dKvLy/H0f7lZ855/GGttZdfD345Pv/w0y9vx5dvnz5/rHx5//3t2/+P4cdUPIP399hP395//ViR4+0QO0Vkph3t9GiieOJs63U/qseXQ/S0iOh2vLZzuIRUSDvel0oVUz9e9RTL6RXSqROaoDMeOrO1WSD7haROv3T6SBsFMoAcZwBImgB2x/4FMHHyPEdGM2i003sHkQI4oDFOA9AvYIhGL4ATB+9r67y2Dp9WAWG2L0RaS1rIzwyd0yukrM3Ve/Y8XgO2FGnVuUWXTtgnoh2viW9jlLYUA9LOYTpNqNOm+az8I3SQnj1zpFOnis+UCrkcdEqb3SZ1zjZsVGaXgN3bqSDXx/EKK3QzK3dP6HyF0tEHifYTfGdNdMD0gMYQ90mm5ple2n5S63KONF3Gnz5mBVW6CQ4PxO9cBFpa7yWUfoKukNBQPjQtkEsVlI6Chdps0oUPKf6tc46ewmFaDNelteVoWhKgq17H2V0RIYRajxgltMMDr/PsCg+sQFWZvbSrxnJBOxGdnkqsz6G9xF7uEiBaG3PZQLy1ksJYWD1n2MxBI8jUKLNKl8NQUCZs4EtvgxOyLD3tgXVkgekKCZtDKr0mD74zNd2ItRAp3Wv6wOZQW+mHEpBlHprRuqx/NnpjrmRkn1UqmH+wlUQtRaapo65VNcj6w2A6WkOVMvqMJaGAxgOKepZwIIJyxWIFzQ/TpvtQlo8BN1SFyK4MQ7B2E1fUJBhYulXQuSzQ2VRQOg4GONqWVg3g8pevYoR81zMtpVVaXRYBFAG0CBQ7+KIPPFZB9aFVIlEt2VjQiSSrc7k91EZHaVNixVC/q8Lp9Fc7+/TwVTlk+Cwrh1/lEH8G2ZWMYV3K/VkOBSUWp56MwTFmlBnuuYoxog6tjTyn4Eupk74CO7WBrIZOb8GzFci5dLpOX9bp2LrsWL2tnuF9oK6DL6xlrQqpLhdLtst+MMFROqow7bo0qpmizPmJbMlWcey2NCr8A+UdZUuzTKd+davsrcdEgE6oLAMUKf4jt0YjR4saqJRuZS73AJBhFJxkAlVF64aORPwfrGLmaECYKtwQblWB6iuNsI7WjBjKcyrqX7k3pwl4BE6GP9H4R/MygtBk3hlr1hwlbTDokMMVUFb45pAWzHX1f5iMQhdQR3cEMiYFRS+vXBO2wlzRINHWFYVGtGz6KCmMcrRPHcInMHNpeZi+osJw2msqCw6E5ZwXV5CjCsF+bEzwYa/SNjjpAWmIn860nYovpYXYlnLNo2OVAsTkaOWB6J1Y0/C8kAiRMiEQBm8cSNHhV3i729TKmCnX5j3NaKN0dPoqfFOvEbdhCkW1PREXs3RP2mNqBhLlk0jMzNX4lh+TuOdEPCEbfUo53OdjEm8TrpwcYh1RUiKvSbwDOC9gA9MKmNfBsXdO7m0Y98rzPCZxWMiXhZAZZQHMec3XjU2a58ZINCrgYFnzZSA8A6DC4VWCD7niElvDPthavZWTwtBLI9xtPKxrZlnOh10cAdQFhNfLlADxn5mEiSnCOA2iRY9S5Q9vX7+72123vc/vb1/v2x7fet/p+Lb74oa/HX7fzgSffl/BBJcruP/Hn/6zH4m1mPedSvAZ981J8Zn3/Uh49Rnfq5B2LYs8XXpkiU2U/X6J+ymSpdh0hXwpNmMhZYp4unzIEpsxhw4K2aRxl5C1snnLIj5xH3i6GQiFbs66DIuf27jkDKGbsy4DG2b1p6ldKHRzVnKG0M2ZIxyFbs6YtJ1CN2fM006hmzOmZqew59nYjzX3Pk3AQmGbs5EzhN0xQc4QtjkbOUPY5mzkDGGbs9HOELY5G+0MYZuz0c5c2ZyNdobw5/lQKXxzdrn4+ObsjGEI35zdLi/7Hcorlh1z2dOEJhSYv76LRidzCN/MncwhfDN3MufKZu60NkR/Hpicom/mfWUTJpjNHCOQU/S9eydziL6ZdzKH6Hce9sfKtnaPx8rm3MkZom/OnZy5sjn3ea3E8yTh3D025yBniNicg5wh4i4BxhU8tTmHX7aNzRltXSlic45VObCyOUeuWhKbcrB8QMSmHKSMlXzurkKRm3IyQCByU05ShshNOUkZIjdlNECnyE0ZjY6/oo09NTSnyLvc5WOvzTmZiBC5ObOlUYznpuMUY3MeNDPE2JwHOUOMzZlj97LRc2NwUrhPOsgZwJvPiMdTtx4WDyT9XWDGCuf2lOKDnMOfAnzSzkiyu5xMudx+B/jUqySDF1Nqr7MtPTch/v7x35X4/vI3ZODjHXZN8rAAAANselRYdFNNSUxFUzIgcmRraXQgMjAyMy4wOS42AAB4nD1UO4+bOQz8K1fagCyIT5ExDgjgw2Er55qrFlttf22a/PgbUt4sAjjfaPgQZ6j3t4/3x/cP4scDv9/fPi6Py58/rs+vj8fj8rwCOOjj8imf9SfX3/95Up+9Ap798UmfdY5cf1/x75/CLj+uOAD+/t/bB10fz1Pp0X/PiqgYKpyqbNejYkjT+I9fF5mxt+W47RnJweMORDhl3HxKiua42yRR9+KQRNC4+2S17cUhIaLiuKzN46ZzO2cWR7YvHTeZ6sS7OUyyx40nuaaOO4qCI80xC3CiOdYcUwKH1iRAjWjsg4Q78uBX91ooRjzlN0SOhkgmEY+F4gu//f2VRvaruLtHx9iOtbtnQNmQCy/vhk4alWTuKNsig+bWxdaExW5D5lLKOA0DaWDLAUJj6MQ0a1b4XokUmIeaaqWwr++1Tme6syMkpOctKKpVVDSsR4kSu2pg/qihXTMKYFymZKwQrxDWuojWPDBrXIX35pMESF9OhFibI2mtYrKRdxqKo5DvRU1xoaw5qqwAhdBt7mKIi9X9pm0q5XES6TLuPPeKGv6amIDYKcUSWSS0h3iQlFM32jFngwfu5YpYgTu4ac0BeWCqwTM9swksAlUwKKhS52yKA5vGHM23hQOfCcIJgBlzBKbTGes6UBoMrerVvGDENDaup4Sc6HnFSrgKWHJu2A0t4hrQFP4PyN5DkKWCxBgGtgeUHbSc0QtrG3lNDshdTgmONsZkFywNT0/isvFcW7guDKWUu1CqaytCJXwliWVwLdQzgWvumCCOQgpKSJMNeRBKYWVFseCAeumoFzQ1igRJsQlZcShb48Ghk8O7OEtxhbpY8ZWLrAKZrFYJSZdHNQW3YXEKgoWVMdBygdVW3W8JEbpTqEJp3Rb0T+3t1ozy4I2gP/J3RdJy2I1KX+0rZ5kcCA6VT0Eze0HajxKuKhl0UuXGQwNInOiVfQeX6QmZVpxAvGIGaDtevq9c1DowlMrTAgeakWp9+0H6hcKEMBl7NbW1zEZ4OpuDERmG4BOeOvVxSHBOzW4dZbTf19FWpkNBE+jTIuQQyHEC3wnaqQevOA59AJHIlnYK8mGPIE+uKAT97Y0GoQ5TLeR1/Pz3Gxz1869vOrWcOaR29Nf/K8dcP5j09zYAAAAASUVORK5CYII=",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Five molecules from the test set:\")\n",
        "indices = np.random.choice(len(test_scaffold), size=3, replace=False)\n",
        "\n",
        "smiles = [test_scaffold.ids[index] for index in indices]\n",
        "mols = [Chem.MolFromSmiles(smile) for smile in smiles]\n",
        "\n",
        "Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(450, 350))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGB2DhhoZ84T"
      },
      "source": [
        "There are certainly functional group differences spread throughout the splits. Notice the presence of the nitrile group in the train set, amine group in the validation set, as well as the sulfonamide group in the test set.\n",
        "\n",
        "Additionally, notice the structural and conformational differences among the various data splits. It will be interesting to see how well our model generalizes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzFIf0RTQNT4"
      },
      "source": [
        "## 4. Model deployment\n",
        "\n",
        "We have successfully generated our train and test datasets. Let's now create a simple MLP model to predict PROTAC degradation properties!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "BhDdqgo6d4mN"
      },
      "outputs": [],
      "source": [
        "# Initialize a 2 layer FCN\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "n_tasks = 1\n",
        "n_features = train_random.X.shape[1]\n",
        "layer_sizes = [256, 32, 1]\n",
        "dropouts = [0.0, 0.2, 0]\n",
        "activation_fns = [nn.ReLU(), nn.ReLU(), nn.Identity()]\n",
        "\n",
        "optimizer = dc.models.optimizers.Adam()\n",
        "\n",
        "# Let's log every train epoch\n",
        "batch_size = 10\n",
        "log_freq = int(len(train_random) / batch_size +1)\n",
        "\n",
        "# L2 loss is default\n",
        "protac_model_random = dc.models.MultitaskRegressor(n_tasks, n_features, layer_sizes, dropouts=dropouts, activation_fns=activation_fns,\n",
        "                                            optimizer=optimizer, batch_size=10, log_frequency=log_freq)\n",
        "protac_model_scaffold = dc.models.MultitaskRegressor(n_tasks, n_features, layer_sizes, dropouts=dropouts, activation_fns=activation_fns,\n",
        "                                            optimizer=optimizer, batch_size=10, log_frequency=log_freq)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AuUhaYURSS8A"
      },
      "source": [
        "Let's now wrap everything together to instantiate a DeepChem model! Note that due to the small sample size, a smaller batch size actually helps performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbwL56cSSRuA",
        "outputId": "446a1620-207b-4f77-ea1a-9d4f8166e1be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 532805 trainable parameters\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "PytorchImpl(\n",
              "  (layers): ModuleList(\n",
              "    (0): Linear(in_features=2048, out_features=256, bias=True)\n",
              "    (1): Linear(in_features=256, out_features=32, bias=True)\n",
              "    (2): Linear(in_features=32, out_features=1, bias=True)\n",
              "  )\n",
              "  (output_layer): Linear(in_features=1, out_features=1, bias=True)\n",
              "  (uncertainty_layer): Linear(in_features=1, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# protac_model = dc.models.torch_models.TorchModel(protac_model, loss=criterion, optimizer=optimizer, batch_size=10, log_frequency=log_freq)\n",
        "param_count = sum(p.numel() for p in protac_model_random.model.parameters() if p.requires_grad)\n",
        "print(\"There are {} trainable parameters\".format(param_count))\n",
        "protac_model_random.model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QF4f6sQNKldg"
      },
      "source": [
        "Let's define the validation function to prevent overfitting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "llgY1tMVS7NU"
      },
      "outputs": [],
      "source": [
        "train_losses_random = []\n",
        "val_losses_random = []\n",
        "\n",
        "train_losses_scaffold = []\n",
        "val_losses_scaffold = []\n",
        "\n",
        "metric = [dc.metrics.Metric(dc.metrics.mean_squared_error)]\n",
        "\n",
        "n_epochs=100\n",
        "for i in range(n_epochs):\n",
        "  protac_model_random.fit(train_random, nb_epoch=1, all_losses=train_losses_random)\n",
        "\n",
        "  protac_model_scaffold.fit(train_scaffold, nb_epoch=1, all_losses=train_losses_scaffold)\n",
        "\n",
        "  # Validate on every other epoch\n",
        "  if i % 2 == 0:\n",
        "    loss = protac_model_random.evaluate(val_random, metrics=metric)\n",
        "    val_losses_random.append(loss['mean_squared_error'])\n",
        "\n",
        "    loss = protac_model_scaffold.evaluate(val_scaffold, metrics=metric)\n",
        "    val_losses_scaffold.append(loss['mean_squared_error'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLxgD85lVd34"
      },
      "source": [
        "We can easily look at how the training went through plotting the recorded losses."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "wEUO8Ba5VdkD",
        "outputId": "dc258d3d-20e3-42f6-a3f6-4933a7065e6e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_steps = [(i+1)*log_freq for i in range(len(train_losses_random))]\n",
        "val_steps = [(i+1)*(log_freq*2) for i in range(len(val_losses_random))]\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "ax[0].plot(train_steps, train_losses_random, label='Train loss')\n",
        "ax[0].plot(val_steps, val_losses_random, label='Val loss')\n",
        "ax[0].legend()\n",
        "ax[0].set_xlabel('Frequency of Steps')\n",
        "ax[0].set_ylabel('Loss')\n",
        "ax[0].set_title('Loss across train and validation random split')\n",
        "\n",
        "ax[1].plot(train_steps, train_losses_scaffold, label='Train loss')\n",
        "ax[1].plot(val_steps, val_losses_scaffold, label='Val loss')\n",
        "ax[1].legend()\n",
        "ax[1].set_xlabel('Frequency of Steps')\n",
        "ax[1].set_ylabel('Loss')\n",
        "ax[1].set_title('Loss across train and validation scaffold split')\n",
        "\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUZst4AjmTuc"
      },
      "source": [
        "We can see that the model performs less well on the scaffold validation set which makes sense as the scaffold splits ensures that more validation molecules are out of distribution relative to the train distribution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-fAPN1Xko1i"
      },
      "source": [
        "Let's now perform some inference on our test set to evaluate our models!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "zeQJaQzQYK28"
      },
      "outputs": [],
      "source": [
        "metrics = [dc.metrics.Metric(dc.metrics.mean_squared_error), dc.metrics.Metric(dc.metrics.pearsonr), dc.metrics.Metric(dc.metrics.pearson_r2_score)]\n",
        "eval_metrics = protac_model_random.evaluate(test_random, metrics)\n",
        "preds = protac_model_random.predict(test_random)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hWgS67TbTik",
        "outputId": "b5340022-16f0-4058-dd84-ceac7fcc9e37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean_squared_error: 3.074001339280645\n",
            "pearsonr: 0.818568671566446\n",
            "pearson_r2_score: 0.6700546700700561\n"
          ]
        }
      ],
      "source": [
        "for k, v in eval_metrics.items():\n",
        "  print('{}: {}'.format(k, v))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "SdaEUlzvnJD8",
        "outputId": "72031818-531b-4a02-e10b-623ea504949c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "preds_and_labels = np.concatenate((test_random.y.reshape([60, 1]), preds.reshape([60, 1])), axis=1)\n",
        "pred_df = pd.DataFrame(preds_and_labels, columns=['Actual log DC50 values', 'Predicted log DC50 values'])\n",
        "sns.jointplot(pred_df, x='Predicted log DC50 values', y='Actual log DC50 values')\n",
        "plt.annotate(f\"R: {eval_metrics['pearsonr']:.2f}\\nR²: {eval_metrics['pearson_r2_score']:.2f}\",\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             ha='left',\n",
        "             va='top',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round,pad=0.5', edgecolor='black', facecolor='white'))\n",
        "# Set the title\n",
        "plt.suptitle('Predicted vs Actual log DC50 Values from Random Split')\n",
        "\n",
        "# Adjust the position of the title to avoid overlap with the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6i7SOIUMnG_5"
      },
      "source": [
        "The random split appears to do fairly well. Let's see how well our model does on the scaffold split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "s-pG0W_bmwzn"
      },
      "outputs": [],
      "source": [
        "metrics = [dc.metrics.Metric(dc.metrics.mean_squared_error), dc.metrics.Metric(dc.metrics.pearsonr), dc.metrics.Metric(dc.metrics.pearson_r2_score)]\n",
        "eval_metrics = protac_model_scaffold.evaluate(test_scaffold, metrics)\n",
        "preds = protac_model_scaffold.predict(test_scaffold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2evgsuEm6r3",
        "outputId": "a4a9e88e-8c1d-4213-d397-0b72a321f14e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean_squared_error: 5.991774828135091\n",
            "pearsonr: -0.10286796793151554\n",
            "pearson_r2_score: 0.010581818826359309\n"
          ]
        }
      ],
      "source": [
        "for k, v in eval_metrics.items():\n",
        "  print('{}: {}'.format(k, v))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "Fts5bKqFa-5A",
        "outputId": "eb44c071-c2c3-4941-a63e-5a44235b1430"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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/oVar873/n3/+GXXr1kWjRo20yxwcHDB06FDcvn0bly9fBvCqoLVUqVJo3769djtbW1sMGTIk3zG8flxNS0J8fDwAZNktl53IyEh89NFH6NChA95//30cPXoUrVq1woIFC3S6L5KSkqBUKvVer+mayK37rGXLlvD09NTp2rp16xZOnjyJXr16aVuwXv/dvXz5EnFxcWjQoAGEEIiKipJ8XtkRQmD79u0ICwuDEELn/deqVSuo1WrtE4H5eV/kZMiQIbC0tNT+rHmfjx07Vqclb8iQIXByctLr9nVwcNCpDatQoQJcXFwQEhKCevXqaZdr/v/vv//OMZ4SJUrgzz//xLBhw/Ds2TOsWLECvXv3hpeXFz777DNt65DUz73Xf4fJycmIi4vTdm3l9rRlds6cOYNHjx5hxIgROt1hbdu2RXBwcJZd4xo///wzgFctWq+T+jkq9fpoWFlZ6bQI2djY4IMPPsCjR49w9uxZSceknDFpysLXX3+N/fv34/Dhw7h8+TL+/vtvtGrVSmcbKysrnZoAALh+/TrUajW8vLzg6emp8y8hIUFbZHjnzh0A0Hvqx9PTE66urjnGpukqrFy5cp7OrTBiBF5dny5duujUnOzYsQNpaWk6SdPEiRPh4OCAunXrIigoCOHh4Thx4oSkc1m/fj0CAgKgVCpx48YN3LhxA+XKlYOdnR02bNig3e7mzZvw8fGBm5ubpP3mx4kTJ9C8eXPY29vDxcUFnp6e2q4AYyRNd+7cQYUKFfSWh4SEaNdr/luuXDm9pDAwMDDfMWgkJCRokyQnJycAyLU7JicKhQLjxo1Denq6Ts2VSqXKsm4pOTlZuz4nVlZW6NGjB3799VdtDYomgdJ0zQFATEwMBgwYADc3Nzg4OMDT01Nb+2KM393jx4/x/PlzrFq1Su+9p6n/07z/8vO+yElAQIDOz5q/lzf/pmxsbFC2bFnteo3SpUvr/U05OzvD19dXbxkAvTrJrHh7e2P58uWIjY1FdHQ0Fi9eDE9PT0ybNg2rV68GIP1z7+nTpxgzZgxKlCgBlUoFT09P7Tnn9XeY3TUCgODgYL1r9OZrLSwsUK5cOZ3lWe0rO1Kuj4aPj49ecb/madTbt29LPiZlj+XyWahbt67OE0NZUSqVejUWmZmZ8PLy0rlhv87T09NoMeZVYcbYs2dPrFy5Env27EHHjh3xv//9D8HBwahWrZp2m5CQEERHR+PHH3/E3r17sX37dixbtgzTpk3DjBkzst13fHw8fvjhByQnJ2f5yPnGjRsxa9Yso7QkZbeP18ccAl59sDdr1gzBwcFYsGABfH19YWNjg59//hlfffUVMjMz8x1LUXHv3j2o1WptEhYYGAgrKytcvHgxX/vV3Hxfrz/y9vZGbGys3raaZT4+Prnut2/fvli6dCk2bdqECRMmYNOmTahYsSKqV68O4NXvskWLFnj69CkmTpyI4OBg2Nvb4/79+xgwYECOvzupfx+affTt21dbcPwmzRAOeX1f5Ca3BDM3r7dSSVn+ZktIThQKBcqXL4/y5cujbdu2CAoKMvhp2O7du+O3337Dhx9+iOrVq8PBwQGZmZl49913Tf79Z4zrQ/nHpMmIypUrhwMHDqBhw4Y5fjj5+/sDeNXqU7ZsWe3yx48f5/rNTPON5dKlSzmOI5LdB3lhxKgRGhoKb29vbNmyBY0aNcKhQ4cwZcoUve3s7e3Ro0cP9OjRA6mpqejcuTNmzZqFyZMnZzvcgmZ8neXLl8PDw0NnXXR0NP773//ixIkTaNSoEcqVK6ctBM6ptSm7a+bq6pplseeb3zB/+OEHpKSkIDIyUqcYWtPlaQz+/v6Ijo7WW3716lXtes1/L1++DCGEznnduHHDKHFoClM1LbB2dnZ45513cOjQIdy9e1ev5UEqTXfO68l79erV8euvvyIzM1Pni8qpU6dgZ2enN65TVurVq4dy5cph48aNaNGiBf766y/MmjVLu/7ixYu4du0avvvuO53C7/379+e6b03L65t/I2/+fWiensrIyJA0BlBe3heG0vy9REdH67zPU1NTcevWLdnGKipbtixcXV21ibGUz71nz57h4MGDmDFjBqZNm6Zdfv369XzF8vo10jxJrREdHa1dn91rMzMzcfPmTZ3Wpazew4Z48/poPHjwQG8oCc2DElk9AZwTjhCeNXbPGVH37t2RkZGBzz77TG9denq69kO1efPmsLa2xpIlS3S+iUmZ7qBmzZoICAjAwoUL9T6kX9+X5k3z5jaFEaOGhYUFunbtih9++AHr1q1Denq6TtccAJ1HgYFX3QIVK1aEEELvyZ3XrV+/HmXLlsWwYcPQtWtXnX8TJkyAg4ODtjWtS5cuEEJk+Q39zWuWVXJUrlw5qNVqXLhwQbssNjZW52k14N9v26/vU61WY+3atdmeh6HatGmDP/74A7///rt22cuXL7Fq1SqUKVMGFStWBPAqmbl//z4iIyO12yUnJ+Obb77JdwyHDh3CZ599hoCAAJ3urenTp0MIgffeey/LkYrPnj2L7777DsCrlqQ3W2LS0tIwZ84c2NjYoGnTptrlXbt2xcOHD7Fjxw7tsri4OGzduhVhYWFZ1jtlpU+fPoiKisL06dOhUCjQu3dv7bqsfndCiCyf5nuTk5MTPDw8cOzYMZ3ly5Yt0/nZ0tISXbp0wfbt2/XGqwJefSHRyOv7wlDNmzeHjY0NFi9erHPuq1evhlqtLvDxq06dOpXl07h//PEHnjx5ok00pHzuZfU7BAz7zMpK7dq14eXlhRUrVuh0E+/ZswdXrlzJ8Rq1bt0aALB48eI8xST1+mikp6dj5cqV2p9TU1OxcuVKeHp6olatWpKOqZHd56G5Y0uTETVu3BgffPABZs+ejfPnz6Nly5awtrbG9evXsXXrVixatAhdu3aFp6cnJkyYgNmzZ6Ndu3Zo06YNoqKisGfPHr1WkzdZWFhg+fLlCAsLQ/Xq1TFw4EB4e3vj6tWr+Ouvv7Bv3z4A0L5BRo8ejVatWsHS0hI9e/YslBhf16NHDyxZsgTTp09HlSpVtLU3Gi1btkTJkiXRsGFDlChRAleuXMHSpUvRtm3bbIuKHzx4gMOHD+sVV2oolUq0atUKW7duxeLFi9G0aVO89957WLx4Ma5fv65tqv/111/RtGlTjBw5UnvNDhw4gAULFsDHxwcBAQGoV68eevbsiYkTJ6JTp04YPXo0EhMTsXz5cpQvX16nuLRly5awsbFBWFgYPvjgAyQkJOCbb76Bl5dXlt1LeTFp0iRs2rQJrVu3xujRo+Hm5obvvvsOt27dwvbt27UtMR988AGWLl2KXr16YcyYMfD29saGDRu0LRRSv0Xu2bMHV69eRXp6Oh4+fIhDhw5h//798Pf3R2RkpE6LR4MGDfD1119jxIgRCA4O1hkR/MiRI4iMjMTMmTMBQPv/Xbt2RUBAAJ4+fYqNGzfi0qVL+Pzzz1GyZEntfrt27Yr69etj4MCBuHz5snZE8IyMDIO6qvr27YtPP/0Uu3fvRsOGDXW+eQcHB6NcuXKYMGEC7t+/DycnJ2zfvl1yq+rgwYMxZ84cDB48GLVr18axY8eyHAphzpw5OHz4MOrVq4chQ4agYsWKePr0Kc6dO4cDBw5ouyXz8r7IC09PT0yePBkzZszAu+++i/bt2yM6OhrLli1DnTp1Cnyk9HXr1mHDhg3o1KkTatWqBRsbG1y5cgVr1qyBra2tth5Qyueek5MTQkND8cUXXyAtLQ2lSpXCL7/8oh1fL6+sra0xd+5cDBw4EI0bN0avXr3w8OFDLFq0CGXKlMG4ceOyfW316tXRq1cvLFu2DGq1Gg0aNMDBgwclt/hKvT4aPj4+mDt3Lm7fvo3y5ctjy5YtOH/+PFatWgVra2uDzju7z0OzV6jP6hVxUh6zFuLVI+j29vbZrl+1apWoVauWUKlUwtHRUVSpUkV89NFH4sGDB9ptMjIyxIwZM4S3t7dQqVSiSZMm4tKlS3qPmL855IDG8ePHRYsWLYSjo6Owt7cXVatWFUuWLNGuT09PF6NGjRKenp5CoVDoPU5qzBhzkpmZKXx9fQUAMXPmTL31K1euFKGhocLd3V0olUpRrlw58eGHHwq1Wp3tPjWP0x88eDDbbSIiIgQAsXv3bu31mDdvnggODhY2NjbC09NTtG7dWpw9e1b7mqtXr4rQ0FChUqn0hlX45ZdfROXKlYWNjY2oUKGCWL9+fZaP6UZGRoqqVasKW1tbUaZMGTF37lyxZs0aAUDcunVLu11ehxwQQoibN2+Krl27ChcXF2Frayvq1q0rfvzxR73X/v3336Jt27ZCpVIJT09P8Z///Eds375dANB5PDwrmveC5p+NjY0oWbKkaNGihVi0aJGIj4/P9rVnz54VvXv3Fj4+PsLa2lq4urqKZs2aie+++077WPuZM2dEWFiYKFWqlLCxsREODg6iUaNG4n//+1+W+3z69KkYNGiQcHd3F3Z2dqJx48a5vk+zUqdOHQFALFu2TG/d5cuXRfPmzYWDg4Pw8PAQQ4YM0Q6lkNvj2YmJiWLQoEHC2dlZODo6iu7du4tHjx5l+Vj5w4cPRXh4uPD19RXW1taiZMmSolmzZmLVqlXabfLyvtBADkMOZHfNli5dKoKDg4W1tbUoUaKEGD58uN6j/Y0bN85yGAR/f3/Rtm1bSXG86cKFC+LDDz8UNWvWFG5ubsLKykp4e3uLbt26iXPnzultn9vn3r1790SnTp2Ei4uLcHZ2Ft26dRMPHjzQ+z0YMuSAxpYtW0SNGjWEUqkUbm5uok+fPtphaDSy+ttISkoSo0ePFu7u7sLe3l6EhYWJu3fvShpywJDro/n9nDlzRrz11lvC1tZW+Pv7i6VLl+psJ3XIgZw+D82ZQggDKvWIyKQtXLgQ48aNw7179/SG0CAi09WkSRPExcVl2fVLxsOaJqJi6s3xi5KTk7Fy5UoEBQUxYSIiygPWNBEVU507d4afnx+qV68OtVqN9evX4+rVq9kON0FERDlj0kRUTLVq1QrffvstNmzYgIyMDFSsWBGbN2/We4KRiIikYU0TERERkQSsaSIiIiKSgEkTERERkQRMmoiIiIgkYNJEREREJAGTJiIiIiIJmDQRERERScCkiYiIiEgCJk1EREREEjBpIiIiIpKASRMRERGRBEyaiIiIiCRg0kREREQkAZMmIiIiIgmYNBERERFJwKSJiIiISAImTUREREQSMGkiIiIikoBJExEREZEETJqIiIiIJLCSOwCinMTExCAuLk7uMPLNw8MDfn5+codBRET5wKSJiqyYmBgEB4cgKSlR7lDyTaWyw9WrV5g4ERGZMCZNVGTFxcUhKSkR9d6fDifvMnKHk2fxsbdxas0MxMXFMWkiIjJhTJqoyHPyLgM3vwpyh0FERGaOSRNRIbly5YrcIeQba7OIyJwxaSIqYEnqJwAU6Nu3r9yh5Btrs4jInDFpIipgaYkvAAhU7z0RngHBcoeTZ6zNIiJzx6SJqJA4ePmxNouIyIRxcEsiIiIiCZg0EREREUnApImIiIhIAiZNRERERBIwaSIiIiKSgEkTERERkQRMmoiIiIgkYNJEREREJAGTJiIiIiIJmDQRERERScCkiYiIiEgCJk1EREREEjBpIiIiIpKASRMRERGRBEyaiIiIiCRg0kREREQkAZMmIiIiIgmYNBERERFJwKSJiIiISAImTUREREQSMGkiIiIikoBJExEREZEEVnIHQAUjJiYGcXFxcoeRL1euXJE7BMpCcfi9eHh4wM/PT+4wiMjEMGkqhmJiYhAcHIKkpES5QzGKtJRUuUMgAEnqJwAU6Nu3r9yh5JtKZYerV68wcSIigzBpKobi4uKQlJSIeu9Ph5N3GbnDybPYi7/jUuQqpKenyx0KAUhLfAFAoHrvifAMCJY7nDyLj72NU2tmIC4ujkkTERmESVMx5uRdBm5+FeQOI8/iY2/LHQJlwcHLz6T/roiI8oqF4EREREQSMGkiIiIikoBJExEREZEETJqIiIiIJGDSRERERCQBkyYiIiIiCZg0EREREUnApImIiIhIAiZNRERERBIwaSIiIiKSgEkTERERkQRMmoiIiIgkYNJEREREJAGTJiIiIiIJrOQOgIhIDleuXJE7hHzz8PCAn5+f3GEQmQ0mTURkVpLUTwAo0LdvX7lDyTeVyg5Xr15h4kRUSJg0EZFZSUt8AUCgeu+J8AwIljucPIuPvY1Ta2YgLi6OSRNRIWHSRERmycHLD25+FeQOg4hMCAvBiYiIiCRg0kREREQkAZMmIiIiIglY00REZMKKw9AJKSkpUCqVcoeRbxwCovhj0kREZIKK09AJUCgAIeSOIt84BETxx6SJiMgEFZehE2Iv/o5LkatM/jw4BIR5YNJERGTCTH3ohPjY2wBM/zzIPDBpekNMTAzi4uLkDiNfikONAxERUVHDpOk1MTExCA4OQVJSotyhGEVaSqrcIRARERUbTJpeExcXh6SkRNR7fzqcvMvIHU6eaWoE0tPT5Q6FiIio2GDSlAUn7zIm3beuqREgIiIi4zHppEkIgRcvXhhtfwkJCQCAp3eikZ6SZLT9Frb42DsAAPX967C2UsgcTd7xPIoWnkfRwvMoWuL/iQHw6j4SHx9vtP06OjpCoTDd61LcKIQw3cEx4uPj4ezsLHcYREREBUKtVsPJyUnuMOj/mXTSZOyWJlMVHx8PX19f3L1716zfXLwOr/A6vMLr8Aqvw79M8VqwpaloMenuOYVCYTJ/+IXBycmJ1wO8Dhq8Dq/wOrzC6/AvXgvKK07YS0RERCQBkyYiIiIiCZg0FQNKpRLTp08vFrOE5wevwyu8Dq/wOrzC6/AvXgvKL5MuBCciIiIqLGxpIiIiIpKASRMRERGRBEyaiIiIiCRg0kREREQkAZMmIiIiIgmYNBERERFJwKSJiIiISAImTUREREQSMGkiIiIikoBJExEREZEETJqIiIiIJGDSRERERCSBSSdNQgjEx8eDcw4TERFlj/dL4zDppOnFixdwdnbGixcv5A6FiIioyOL90jhMOmkiIiIiKixMmoiIiIgkYNJEREREJAGTJiIiIiIJmDQRERERScCkiYiIiEgCJk1EREREEsiaNGVkZGDq1KkICAiASqVCuXLl8Nlnn3HwLSIiIipyrOQ8+Ny5c7F8+XJ89913qFSpEs6cOYOBAwfC2dkZo0ePljM0IiIiIh2yJk2//fYbOnTogLZt2wIAypQpg02bNuGPP/6QMywIIZCWliZrDEWFhYUFrKxk/TMhIiIqEmS9GzZo0ACrVq3CtWvXUL58efz55584fvw4FixYUKhxPH/+HLt378bWrVtx6tQpPH/+HOnp6YUaQ1FmZ2eHUqVKISwsDN26dUO9evWgUCjkDouIiKhQyZo0TZo0CfHx8QgODoalpSUyMjIwa9Ys9OnTJ8vtU1JSkJKSov05Pj4+X8cXQuCjjz7CokWLkJaWhoYNG2LUqFHw8vKCnZ0dEwMA6enpUKvViI6OxoYNG7BgwQL4+/tj586dqFGjhtzhERFRFox9v6T/J2S0adMmUbp0abFp0yZx4cIF8f333ws3NzcRERGR5fbTp08XAPT+qdVqg4+dmZkpRo0aJQCI6dOni3v37uX3dIq99PR0cfjwYVG7dm3h5uYmzp07J3dIRESUBWPeL+lfCiHke1TN19cXkyZNQnh4uHbZzJkzsX79ely9elVv+6wyZ19fX6jVajg5ORl07OnTp+PTTz/FihUr8MEHH+T9JMzQ8+fP0bJlS9y8eRPnzp2Dv79/jturE1MRl5CK+OQ0OKms4WFvA2c7m0KKlojI/Bjzfkn/krV7LjExERYWuqMeWFpaIjMzM8vtlUollEplvo+blpaGpUuXYuzYsUyY8sDFxQX79u2Dn58f1q9fjylTpmS77YPnSZi4/QJ+vR6nXRYa5IE5XarCx0VVGOESEZkdY90vSZes4zSFhYVh1qxZ+Omnn3D79m3s3LkTCxYsQKdOnQr0uIcPH8bTp0/x3nvvFehxijNXV1eEhYXhf//7X7bbqBNT9RImADh2PQ6Ttl+AOjG1oMMkIiIyGllbmpYsWYKpU6dixIgRePToEXx8fPDBBx9g2rRpBXrcrVu3oly5cixkzqdu3bph06ZNiI6ORoUKFfTWxyWk6iVMGseuxyEuIZXddEREhej8+fNwcHDIdr2Hhwf8/PwKMSLTImvS5OjoiIULF2LhwoWFetwLFy4gNDSUT8flU2hoKADg4sWLWSZN8ck5j3X1Ipf1RERkXI0bN85xvUplh6tXrzBxyoZZjlr47NkzuLu7yx2GyXNxcQHw6npmxcnWOsfXO+aynoiIjKtW30lw89f/kgsA8bG3cWrNDMTFxTFpyoZZJk0pKSlQqViEnF+WlpZQKpVITk7Ocr2Hgw1CgzxwLIsuutAgD3g4sGuOiKgwOZX0g5tf1kkT5U7WQvCiLiIiAgqFQvvPysoKpUqVwoABA3D//v0COWZKSgomTpwIHx8fqFQq1KtXD/v375f02ujoaIwbNw4NGjSAra0tFAoFbt++ne32kZGRqFmzJmxtbeHn54fp06cbPBJ6Tl2cznY2mNOlKkKDPHSWhwZ5YG6XqqxnIiIik2KWLU2G+vTTTxEQEIDk5GScPHkSEREROH78OC5dugRbW1ujHmvAgAHYtm0bxo4di6CgIERERKBNmzY4fPgwGjVqlONrf//9dyxevBgVK1ZESEgIzp8/n+22e/bsQceOHdGkSRMsWbIEFy9exMyZM/Ho0SMsX77caOfj46LCkl41EJeQihfJaXC0tYaHA8dpIiIi08OkSYLWrVujdu3aAIDBgwfDw8MDc+fORWRkJLp372604/zxxx/YvHkz5s2bhwkTJgAA+vXrh8qVK+Ojjz7Cb7/9luPr27dvj+fPn8PR0RFffvlljknThAkTULVqVfzyyy/aCXmdnJzw+eefY8yYMQgODjbaeTnbMUkiIiLTx+65PHj77bcBADdv3tRZfvPmTb1lhti2bRssLS0xdOhQ7TJbW1sMGjQIv//+O+7evZvj693c3ODo6JjrcS5fvozLly9j6NCh2oQJAEaMGAEhBLZt25bncyAiIiqu2NKUB5o6IVdXV53lzZo101lvqKioKJQvX15viPu6desCeDW+hq+vb572/eZxAGhbzzR8fHxQunRp7XoiIiL6F5MmCdRqNeLi4pCcnIxTp05hxowZUCqVaNeunVGPExsbC29vb73lmmUPHjww2nFe3++bxzLWcYiIiIoTJk0SNG/eXOfnMmXKYP369ShdurTO8ry2MGkkJSVlOVeQptg8KSkpX/t//TgAsj1WfHy8UY5DRERUnDBpkuDrr79G+fLloVarsWbNGhw7dizPEyFmZGTg8ePHOsvc3NxgY2MDlUqlMyu1hmYcJGONLaXZT3bH4hhWRERE+lgILkHdunXRvHlzdOnSBZGRkahcuTJ69+6NhIQEg/d19+5deHt76/zTPBXn7e2t7Tp7nWaZj49P/k7k/2m65bI7lrGOQ0REVJwwaTKQpaUlZs+ejQcPHmDp0qUGv75kyZLYv3+/zr9q1aoBAKpXr45r167pdY+dOnVKu94YNPs5c+aMzvIHDx7g3r17RjsOERFRccKkKQ+aNGmCunXrYuHChTpTiEgZcsDW1hbNmzfX+ad5Cq9r167IyMjAqlWrtNunpKRg7dq1qFevns6TczExMbh69Wqe4q9UqRKCg4OxatUqZGRkaJcvX74cCoUCXbt2zdN+iYiIijPWNOXRhx9+iG7duiEiIgLDhg0DkP8hB+rVq4du3bph8uTJePToEQIDA/Hdd9/h9u3bWL16tc62/fr1w9GjRyGE0C5Tq9VYsmQJAODEiRMAgKVLl8LFxQUuLi4YOXKkdtt58+ahffv2aNmyJXr27IlLly5h6dKlGDx4MEJCQvIUPxERUXHGpCmPOnfujHLlyuHLL7/EkCFDYGlpaZT9fv/995g6dSrWrVuHZ8+eoWrVqvjxxx8RGhqa62ufPXuGqVOn6iybP38+AMDf318naWrXrh127NiBGTNmYNSoUfD09MTHH3+MadOmGeU8iIiIihsmTTkYMGAABgwYkOU6CwsL3LhxQ2dZfoccAF51382bNw/z5s3LcbsjR47oLStTpoxOy1NuOnbsiI4dOxoYIRERkXliTRMRERGRBEyaiIiIiCRg0kREREQkAZMmIiIiIgmYNBERERFJwKSJiIiISAImTUREREQSMGnKQUREBBQKhfaflZUVSpUqhQEDBuD+/fs6227evBkNGjRA48aNUalSJXz77bf5OnZkZCRq1qwJW1tb+Pn5Yfr06UhPT5f02szMTHzxxRcICAiAra0tqlatik2bNult98cff2DEiBGoVasWrK2toVAo8hUzERFRccbBLSX49NNPERAQgOTkZJw8eRIRERE4fvw4Ll26BFtbWwCvpkA5evQorK2tcf78edSsWRPNmzdHmTJlDD7enj170LFjRzRp0gRLlizBxYsXMXPmTDx69AjLly/P9fVTpkzBnDlzMGTIENSpUwe7d+9G7969oVAo0LNnT+12P//8M7799ltUrVoVZcuWxbVr1wyOlYiIyFwwaZKgdevWqF27NgBg8ODB8PDwwNy5cxEZGYnu3bsDAAICArTbCyG0rVN5MWHCBFStWhW//PILrKxe/YqcnJzw+eefY8yYMQgODs72tffv38f8+fMRHh6OpUuXamNu3Lixdr48zZQvw4cPx8SJE6FSqTBy5EgmTURERDlg91wevP322wCAmzdv6q178eIF+vfvjzFjxsDf31+7PDY2FlevXkVaWlqO+758+TIuX76MoUOHahMmABgxYgSEENi2bVuOr9+9ezfS0tIwYsQI7TKFQoHhw4fj3r17+P3337XLS5QoAZVKlfPJEhEREQAmTXmimWPO1dVVZ3lSUhI6duyIwMBAvbnjJk+ejJCQEL1aqDdFRUUBgLZlS8PHxwelS5fWrs/p9fb29ggJCdFZXrduXZ39ExERkWHYPSeBWq1GXFwckpOTcerUKcyYMQNKpRLt2rXTbpOUlIT27dvD29sbERER2i4wQ8XGxgIAvL299dZ5e3vjwYMHub6+RIkSel2Dmv3l9noiIiLKGluaJGjevDk8PT3h6+uLrl27wt7eHpGRkShdurR2m5kzZ+LQoUO4e/cumjdvjiZNmuh0hUVEREAIkWtheFJSEgBAqVTqrbO1tdWuz+n12b329f0TERGRYdjSJMHXX3+N8uXLQ61WY82aNTh27JheYjJr1izMmjUr38fS1BilpKTorUtOTs61BkmlUmX72tf3T0RERIZhS5MEdevWRfPmzdGlSxdERkaicuXK6N27NxISEox+LE03mqab7nWxsbHw8fHJ9fX//PMPhBB6rwWQ6+uJiIgoa0yaDGRpaYnZs2fjwYMH2kf6jal69eoAgDNnzugsf/DgAe7du6ddn9PrExMTceXKFZ3lp06d0tk/ERERGYZJUx40adIEdevWxcKFC7XdXrmROuRApUqVEBwcjFWrViEjI0O7fPny5VAoFOjatat2mVqtxtWrV6FWq7XLOnToAGtrayxbtky7TAiBFStWoFSpUmjQoIHU0yQiIqLXsKYpjzQDRUZERGDYsGG5bj958mR89913uHXrVq7F4PPmzUP79u3RsmVL9OzZE5cuXcLSpUsxePBgnaEEdu7ciYEDB2Lt2rUYMGAAAKB06dIYO3Ys5s2bh7S0NNSpUwe7du3Cr7/+ig0bNug81Xfnzh2sW7cOwL8tWzNnzgQA+Pv747333jPkkhARERVrTJryqHPnzihXrhy+/PJLDBkyJM9DDGSlXbt22LFjB2bMmIFRo0bB09MTH3/8MaZNmybp9XPmzIGrqytWrlyJiIgIBAUFYf369ejdu7fOdrdu3cLUqVN1lml+bty4MZMmIiKi1yjEmxXDJiQ+Ph7Ozs5Qq9VwcnKS/Dp/f3/0798fn376aQFGZx5UKhW++OILjBo1Su5QiIgoG5r7ZdMJy+AVVD3LbZ7GRGP/rIE4e/YsatasWbgBmgjWNBERERFJwKSJiIiISAKzTZpMuFeySOF1JCIic2GWSZOdnR1evnwpdxgmLy0tDSkpKbC3t5c7FCIiogJnlkmTm5sbHj58KHcYJu/x48cAAFdXV5kjISIiKnhmmTQ1aNAA+/fvR3p6utyhmLS9e/dCoVDgrbfekjsUIiKiAmeWSVO3bt3w+PFjHDt2TO5QTNrWrVsRGhqKkiVLyh0KERFRgTPLpKlOnTrw9/fH8uXLWcicR9euXcOBAwfQrVs3uUMhIiIqFGaZNCkUCnz66afYtm0bwsPDmTgZ6Nq1a2jatCmCgoLQq1cvucMhIiIqFGY7jUq/fv2QlpaGwYMH48GDBxgyZAhatGgBGxsbuUMrsmJiYrBt2zZ8+eWXcHFxweHDh+Hm5iZ3WERERIXCbJMmABg0aBBsbW0xc+ZMtGvXDi4uLmjSpAk8PDxgb28PhUIhd4iyS09Px/Pnz3H9+nWcOnUKSqUSbdq0wfLly1GiRAm5wyMiIio0Zp00AUCfPn3Qu3dvXLp0CVu3bsWpU6fw559/chyn/2dhYQEXFxf4+/tj1KhRCAsLM2iePyIiouLC7JMm4FWNU5UqVVClShW5QyEiIqIiyiwLwYmIiIgMxZYmIiIiMxF35xrS0rN+YjzhUQwA4MqVK4UZkg4PDw/4+fnJdvzcKIQJP28fHx8PZ2dnqNVq1tkQERFlQ3O/zJ0CgHxpgUplh6tXrxTZxIktTURERGYipPUAOHn7Z7ve2s4RKmf3QozoX/Gxt3FqzQzExcUxaSIiIiJ5laxUF15B1eUOw2QxaSIyQ+rEVMQlpCI+OQ1OKmt42NvA2Y4DuxIR5YRJE5GZefA8CRO3X8Cv1+O0y0KDPDCnS1X4uKhkjIyIqGjjkANEZkSdmKqXMAHAsetxmLT9AtSJqTJFRkRU9DFpIjIjcQmpegmTxrHrcYhLYNJERJQdds+RyWE9Tt7FJ6fluP5FLuuJiMwZkyYyKazHyR8nW+sc1zvmsp6IyJyxe45MButx8s/DwQahQR5ZrgsN8oCHA1vsiIiyw6SJTAbrcfLP2c4Gc7pU1UucQoM8MLdLVXZzEhHlgN1zZDJYj2McPi4qLOlVA3EJqXiRnAZHW2t4OBTfujDWwBGRsTBpIpPBehzjcbYzj8SBNXBEZEzsniOTwXocMgRr4IjI2Jg0kclgPQ4ZgjVwRGRs7J4jk2Ju9TiUd6yBIyJjY9JEJsdc6nEof1gDR0TGxu45IiqWWANHRMbGpImIiiXWwBGRsbF7joiKLdbAEZExyd7SdP/+ffTt2xfu7u5QqVSoUqUKzpw5I3dYRFRMONvZoJyXA6r7uaKclwMTJiLKM1lbmp49e4aGDRuiadOm2LNnDzw9PXH9+nW4urrKGRYRERGRHlmTprlz58LX1xdr167VLgsICJAxIiIiIqKsydo9FxkZidq1a6Nbt27w8vJCjRo18M0332S7fUpKCuLj43X+UcFTJ6bi5qMERMU8w83HCRxJmYioiOP9smDI2tL0999/Y/ny5Rg/fjw+/vhjnD59GqNHj4aNjQ369++vt/3s2bMxY8YMGSI1X5y7i8wNJ/il4oD3y4KhEEIIuQ5uY2OD2rVr47ffftMuGz16NE6fPo3ff/9db/uUlBSkpKRof46Pj4evry/UajWcnJwKJWZzok5MxchNUVlORREa5IElvWrwZkLFCr8kUHGR3f2y6YRl8AqqLl9gOXgaE439swbi7NmzqFmzptzhZEnW7jlvb29UrFhRZ1lISAhiYmKy3F6pVMLJyUnnHxUczt1F5oQT/FJxwvtlwZA1aWrYsCGio6N1ll27dg3+/v4yRUSv49xdZE74JYGIciNr0jRu3DicPHkSn3/+OW7cuIGNGzdi1apVCA8PlzMs+n+cu4vMCb8kEFFuZE2a6tSpg507d2LTpk2oXLkyPvvsMyxcuBB9+vSRMyz6f5y7i8wJvyQQUW5kHxG8Xbt2uHjxIpKTk3HlyhUMGTJE7pDo/3HuLjIn/JJARLnh3HOUI87dReZC8yVh0vYLOPbG03P8kkBEAJMmksDZjkkSmQd+SSCinDBpIiJ6Db8kEFF2ZK9pIiIiIjIFTJqIiIiIJGDSRERERCQBa5qIzBQnpiUiMgyTJiryeHM3Pk5MS0RkOCZNVKTx5m58uU1Mu6RXDSalRERZYE0TFVlyzzqvTkzFzUcJiIp5hpuPE4rNLPecmJaIKG/Y0kRFlpSbe0G1iBTnFi5OTEtElDdsaaIiS66bu9wtXAWNE9MSEeUNkyYqsuS6uRf37itOTEtElDdMmqjIkuvmXty7rzQT0755bTkxLRFRzljTREWWXLPOm0P3FSemJSIyHJMmKtLkuLlrWriOZdFFV9S7rwwZ04oT0xIRGYZJExV5hX1zl6uFK7+K8xN/RERFAZMmoiyYWvcVB6wkIip4TJqIsmFK3VdyjmlFRGQu+PQcUTFQ3J/4IyIqCpg0ERUD5vDEHxGR3Ng9R1QMmPITf0RUeOLuXENaupA7jCwlPIoBAFy5cqXQjunh4QE/Pz/J2yuEEAZdvaSkJAghYGdnBwC4c+cOdu7ciYoVK6Jly5aGRZtP8fHxcHZ2hlqthpOTU6Eem6ioefA8Kdsn/rxN4Ok5Q4ZLICLDaO6XRZ8CQOEldSqVHa5evSI5cTK4palDhw7o3Lkzhg0bhufPn6NevXqwtrZGXFwcFixYgOHDhxscNJmfgrhBmvtN19Se+Hsdh0sgKhwhrQfAydtf7jCyZW3nCJWze6EcKz72Nk6tmYG4uLiCS5rOnTuHr776CgCwbds2lChRAlFRUdi+fTumTZvGpIlyVRA3SN50XzGlJ/40OFwCUeEpWakuvIKqyx2GyTK4EDwxMRGOjo4AgF9++QWdO3eGhYUF6tevjzt37hg9QCpecrtBqhMNnwy3IPZJhae4T5BMRMWHwUlTYGAgdu3ahbt372Lfvn3aOqZHjx6xrohyVRA3SN50TRuHSyAiU2Fw0jRt2jRMmDABZcqUQd26dfHWW28BeNXqVKNGDaMHSKZNnZiKm48SEBXzDDcfJ0CdlHMCk5cbJG+6po3DJRCRqTC4pqlr165o1KgRYmNjUa1aNe3yZs2aoVOnTkYNjkxbVnVGGwfXy/E1eblB8qZr2jhcAhGZijwNblmyZEk4Ojpi//79SEpKAgDUqVMHwcHBRg2OTFd2dUa//f0EjQKzfjIirzdIzU3XmPukwqOZIPnN32FRnyCZiMyPwS1NT548Qffu3XH48GEoFApcv34dZcuWxaBBg+Dq6or58+cXRJwko7w8yp9dndGa47ewuFcNKBQKvSfd8nqD1Nx0sxujiDfdos+Uh0sgIvNhcNI0btw4WFtbIyYmBiEhIdrlPXr0wPjx45k0FTN5fZQ/uzqjxNQMjN4Uhd3hDWGhUBjtBsmbruGK2rhWpjhcAhGZF4OTpl9++QX79u1D6dKldZYHBQVxyIFiJj/j5+RUZ5SYmgELhQLlvByMGi9vutJxXCsiIsMZXNP08uVL7RQqr3v69CmUSqVRgqKiIT+P8rPOqOjiuFZERHljcNL09ttv4/vvv9f+rFAokJmZiS+++AJNmzY1anAkr/w8ys/i3qKL41oREeWNwd1zX3zxBZo1a4YzZ84gNTUVH330Ef766y88ffoUJ06cKIgYSSb5fZSfdUZFE8e1IiLKG4OTpsqVK+PatWtYunQpHB0dkZCQgM6dOyM8PBze3t4FESPJxBjj57DOqOgxNBkuagXjRERyMThpAgBnZ2dMmTLF2LFQEcNH+YsnQ5JhFowTEf3L4KTp2LFjOa4PDQ3NczBU9LCLrfiRmgzn5+lJIqLiyOCkqUmTJnrLFAqF9v8zMjLyFRAVPexiK36kJMNSCsb5d0FE5sTgpOnZs2c6P6elpSEqKgpTp07FrFmzjBYYERWs3JJhFowTEekyOGlydnbWW9aiRQvY2Nhg/PjxOHv2rFECIyJ5cSJkIiJdeZqwNyslSpRAdHS0sXZHRDLjAKVERLoMbmm6cOGCzs9CCMTGxmLOnDmoXr26seIiIpnx6UkiIl0GJ03Vq1eHQqGAEEJnef369bFmzRqjBUZE8uPTk0RE/zI4abp165bOzxYWFvD09IStra3RgiKiooNPTxIRvWJw0uTv718QcRAREREVaZKSpsWLF0ve4ejRo/McDBFRUcNpZIhIQ1LS9NVXX0namUKhYNJERMUGp5EhotdJSprerGMiIiruOI0MEb3JaOM0EREVJ1KmkSEi82JwITgA3Lt3D5GRkYiJiUFqqu4Hx4IFC4wSGBU+c63dMNfzloMpXWtOI0NEbzI4aTp48CDat2+PsmXL4urVq6hcuTJu374NIQRq1qxZEDFSITDX2g1zPW85mNq15jQyRPQmg7vnJk+ejAkTJuDixYuwtbXF9u3bcffuXTRu3BjdunUriBipgOVWu6FOLLrdEOrEVNx8lIComGe4+TjBoFhN+bxNjSlea04jQ0RvMjhpunLlCvr16wcAsLKyQlJSEhwcHPDpp59i7ty5Rg+QCp6p1m48eJ6EkZui0GzBUXRa9huazT+KUZui8OB5kqTXm+p5myJTvNaaaWTeTJw4jQyR+TK4e87e3l5bx+Tt7Y2bN2+iUqVKAIC4uKw/FKloM8XaDWM82RSfnAY7G0u83ygANXxdkJKeCVtrS5yLeYY1x28VyfM2Vab4NwZwGhki0mVw0lS/fn0cP34cISEhaNOmDf7zn//g4sWL2LFjB+rXr18QMVIBM8XaDSktF7nd2JxV1ljcqwbWnriFpYduaJc3DHTH4l414KQqeudd0AqqUNsU/8Y0OI0MEWkYnDQtWLAACQkJAIAZM2YgISEBW7ZsQVBQEJ+cM1Ga2o1jWSQhRbV2wxgtF/ZKK6w9cQsnbjzRWX7ixhMoAMzvXj0fEZqegizUNsW/MSKiNxlc01S2bFlUrVoVwKuuuhUrVuDChQvYvn0756UzUaZYu2GMlouE5HS9hEnj+I0nSEhOz1NspqigC7VN8W+MiOhNBrc0DR48GH379kWTJk0KIBySi6nVbhij5cJU62wKgjG6O3Njan9jRERvMjhpevz4Md599114enqiZ8+e6Nu3L6pVq1YQsVEhM6XaDU3LxaTtF3QSJ0NaLky5zsbYCiuBNKW/MSKiNxmcNO3evRvPnj3D1q1bsXHjRixYsADBwcHo06cPevfujTJlyhRAmET68ttywTqbfzGBJCLKXZ7mnnN1dcXQoUNx5MgR3LlzBwMGDMC6desQGBho7PiIcuRsZ4NyXg6o7ueKcl4OBrVisM7mXxzI0bTlZ5BXIpIuT3PPaaSlpeHMmTM4deoUbt++jRIlShgrLqJCwTqbV4zR3UnyMLXpaYhMWZ6SpsOHD2Pjxo3Yvn07MjMz0blzZ/z444945513jB0fUYFjnc0rTCBNjzEGeSUi6QxOmkqVKoWnT5/i3XffxapVqxAWFgalUlkQsRFRIWMCaVoK46lHIvqXwUnTJ598gm7dusHFxaUAwiEiIqk4bAZR4TI4aRoyZEhBxEFERAbiU49EhStPT88REZH8+NQjUeFi0kREZKI4bAZR4crXkANERCQvPvVIVHiKTEvTnDlzoFAoMHbsWLlDISIyKfkZ5JWIpDOopSk1NRW7du3C77//jn/++QcAULJkSTRo0AAdOnSAjU3e3qinT5/GypUrUbVq1Ty9noiIiKigSW5punHjBkJCQtC/f39ERUUhMzMTmZmZiIqKQr9+/VCpUiXcuHHD4AASEhLQp08ffPPNN3B1dTX49URERESFQXJL0/Dhw1GlShVERUXByclJZ118fDz69euH8PBw7Nu3z6AAwsPD0bZtWzRv3hwzZ8406LVEREQkXdyda0hLF3KHUeCsrKxgrcy59ys+9rbh+5W64YkTJ/DHH3/oJUwA4OTkhM8++wz16tUz6OCbN2/GuXPncPr0aUnbp6SkICUlRftzfHy8QccjIiIyB9ndLy9uXShTREWTSmUHD4+sh+3IiuSkycXFBbdv30blypWzXH/79m2DRgm/e/cuxowZg/3798PW1lbSa2bPno0ZM2ZIPgYREZE5yu5+GdJ6AJy8/WWIqPC8jIvFpchVWL9+PUJCQnLc1sPDA35+fpL3rRBCSGqnmzZtGpYuXYqpU6eiWbNmKFGiBADg4cOHOHjwIGbOnIlRo0bhk08+kXTgXbt2oVOnTrC0tNQuy8jIgEKhgIWFBVJSUnTWAVlnzr6+vlCr1Vm2gBHJTZ2YiriEVMQnp8FJZQ0Pez4KTkQFL7v7ZdMJy+AVVF2+wArB05ho7J81EGfPnkXNmjWNum/JLU2ffvop7O3tMW/ePPznP/+BQqEAAAghULJkSUycOBEfffSR5AM3a9YMFy9e1Fk2cOBABAcHY+LEiXoJEwAolUpODkwm48HzJL0Z6EODPDCnS1X4uKhkjIyIijveLwuGQUMOTJw4ERMnTsStW7d0hhwICAgw+MCOjo56XX329vZwd3fPtguQyFSoE1P1Eibg1czzk7ZfwJJeNdjiRERkYvI0InhAQECeEiUicxGXkKqXMGkcux6HuIRUJk1ERCYmz9OoPHjwACtXrsSNGzfg7e2NwYMHIzg4OF/BHDlyJF+vJyoq4pPTclz/Ipf1RERU9Ege3NLOzg6PHz8GAFy+fBkVK1bExo0bkZaWhp9++gm1atXChQsXCixQIlPiZGud43rHXNYTEVHRIzlpSk5OhuZBu48//hihoaG4cuUK/ve//+Gvv/5C+/btMWXKlAILlMiUeDjY6M08rxEa5AEPB3bNERGZmjxN2Hvu3Dl8+OGHsLJ61btnYWGBjz76CGfPnjVqcESmytnOBnO6VNVLnEKDPDC3S1XWMxUCdWIqbj5KQFTMM9x8nAB1YqrcIRGRiZNc06RQKLTDDFhYWMDZ2VlnvYuLC549e2bc6IhMmI+LCkt61UBcQipeJKfB0dYaHg4cp6kwcLgHIioIkluahBAoX7483Nzc8ODBA736pRs3bqBkyZJGD5DIlDnb2aCclwOq+7minJcDE6ZCkNtwD2xxIqK8ktzStHbtWp2fAwMDdX4+efIkOnXqZJyoiEwIR/0uWjjcAxEVFMlJU//+/XNcP3Xq1HwHQ2Rq2A1U9HC4ByIqKAY9PRcZGYkXL17orYuPj0dkZKTOPDdExR27gYomDvdARAVFctK0cuVKLFq0CI6OjnrrnJycsHjxYnzzzTdGDY6oKMutG+j+8yQ+tSUDDvdARAVFctK0YcMGjB07Ntv1Y8eOxffff2+MmIhMQm7dQLefJKLZ/KMYtSkKD54nFVJUxOEeiKigSK5pun79OqpVq5bt+qpVq+L69etGCYrIFOTWDaS0evWdhJP0Fj4O90BEBUFyS1N6erp2GpWsPH78GOnp6UYJisgU5NQN1DDQHVF3n2t/1jy1RYWHwz0QkbFJTpoqVaqEAwcOZLv+l19+QaVKlYwSFJEpyK4bqGGgOwY2DMCa47d0lvOpLSLpOKI7FUWSu+fef/99jB8/HpUqVUK7du101v3www+YNWsWFixYYPQAiYqy17uBniWmQp2Uhqi7zzF6UxQSUzN0tjXVp7Y4DhUVNg7lQUWV5KRp6NChOHbsGNq3b4/g4GBUqFABAHD16lVcu3YN3bt3x9ChQwssUKKiytnuVRKhTkzFqE1ROJbFE3Wm+tQWb15U2HIbyoO1gSQngybsXb9+PTZv3ozy5cvj2rVriI6ORoUKFbBp0yZs2rSpoGIkMgnF7aktjkNFcpAyojuRXCS3NGl0794d3bt3L4hYiExecXpqi9ORkBw4ojsVZQYnTWq1Gvv378ft27ehUChQtmxZNGvWDE5OTgURH5HJ0XTXmTrevEgOHNGdijKDkqb169dj5MiRiI+P11nu7OyMFStWoEePHkYNjojkw5sXyUEzlEdxqg2k4kNyTdO5c+cwcOBAdOzYEVFRUUhKSkJiYiLOnDmDsLAwvPfee/jzzz8LMlYis1eYj2FzOhKSgynWBnJ4BPMhuaVpyZIl6NixIyIiInSW16xZE99//z0SExOxaNEirFmzxtgxEhEK/0k2zc1r0vYLOt/6i/LNi4oHU6oN5BOm5kVy0nTixAksW7Ys2/XDhg3DiBEjjBIUEemS6zFsU7p5UfFiCrWBHB7B/EhOmh48eIDy5ctnu758+fK4f/++UYIiIl1yPslmCjcvIjnwCVPzIzlpSkxMhK2tbbbrlUolkpOTjRIUEekyhSfZOHI4mRtTeF+ScRn09Ny+ffvg7Oyc5brnz58bIx4iykJRf5KNdR1kjor6+5KMz6CkqX///jmuVygU+QqGqLjStMIkpKTBxc4GqemZSEhJl9wiU5Qfw2ZdB5mrovy+pIIheciBzMzMXP9lZGTkviMiM/PgeRJGbopC2NLjeJyQio93XkSLr46h07Lf0Gz+UYzaFIUHz5Ny3EdRfgyb016QuSrK70sqGAaPCE5E0r3eCjPynUCsPXELJ2480dlGaotMUX2SjXUdZM6K6vuSCgaTJqIC9HorTA1fFyw9dCPL7aQ+aVMUn2RjXQeZu6L4vqSCIbl7jogM93orTEp6Zo7bmmqLDEcOJyJzwaSJqAC93gqjtMr57aaysSzocAoE6zqIyFywe46oAL3+dE3U3edoGOiuV9MEAA0D3XEu5jlKOtmaZJLBug4iMgdsaSIqQK+3wqw5fgsDGwagYaC7zjYNA90xsGEAPvvxskk/aeZsZ4NyXg6o7ueKcl4OTJiIqNgxuKXJ1dU1y/GYFAoFbG1tERgYiAEDBmDgwIFGCZDI1L3eCvMsMRUT3w1GanomHiekwMbSAlF3n2P0pigkpmaYbF0TEZE5MDhpmjZtGmbNmoXWrVujbt26AIA//vgDe/fuRXh4OG7duoXhw4cjPT0dQ4YMMXrARKZI83TNzUcJaLbgaLbb8UkzIqKiy+Ck6fjx45g5cyaGDRums3zlypX45ZdfsH37dlStWhWLFy9m0kT0Bo4gTERkugyuadq3bx+aN2+ut7xZs2bYt28fAKBNmzb4+++/8x8dUTH0aYfKeJtPmhERmRyDW5rc3Nzwww8/YNy4cTrLf/jhB7i5uQEAXr58CUdHR+NESFRMaCa1PXvnGd5vFIABDcoAAEq7qkz2qTkiInNicNI0depUDB8+HIcPH9bWNJ0+fRo///wzVqxYAQDYv38/GjdubNxIiUzYm5Pavj4yeGiQB5b0qiFXaEREJJHBSdOQIUNQsWJFLF26FDt27AAAVKhQAUePHkWDBg0AAP/5z3+MGyWRiZMyqS1bmoiIirY8DW7ZsGFDNGzY0NixEBVbnNSWiMj05SlpysjIwK5du3DlyhUAQKVKldC+fXtYWprmNBBEBY2T2hIRmT6Dk6YbN26gTZs2uH//PipUqAAAmD17Nnx9ffHTTz+hXLlyRg+SyNRxqAEiItNn8JADo0ePRrly5XD37l2cO3cO586dQ0xMDAICAjB69OiCiJHI5BX2pLbqxFTcfJSAqJhnuPk4AepE052ehYioqDC4peno0aM4efKkdngBAHB3d8ecOXNY50SUg8Ka1FYztMHrheehQR6Y06UqfFxURj0WEZE5MThpUiqVePHihd7yhIQE2Niwi4EoJ5rpVArKm0MbaBy7HodJ2y9gSa8afEqPyIzF3bmGtHQhdxhGZ2VlBWvlq8+2+NjbBXccQ1/Qrl07DB06FKtXr9aO03Tq1CkMGzYM7du3N3qARCQdhzYgopxc3LpQ7hAKhUplBw8Pj9w3NJDBSdPixYvRv39/vPXWW7C2fvXET3p6Otq3b49FixYZPUAiko5DGxBRTkJaD4CTt7/cYRjVy7hYXIpchfXr1yMkJAQA4OHhAT8/P6Mfy+CkycXFBbt378b169dx9epVAEBISAgCAwONHhwRGYZDGxBRTkpWqguvoOpyh2FUT2OicSlyFUJCQlCzZs0CPVaexmkCgKCgIAQFBRkzFiLKJw5tQERUcCQlTePHj5e8wwULFuQ5GCLKH83QBpO2X9BJnApqaAMiInMiKWmKioqStDOFQpGvYIgo/wpraAMiInMjKWk6fPhwQcdBREZU0EMbEBGZozzXNBGZCnViKuISUhGfnAYnlTU87JlQEBGR4Zg0UbHG0bGJiMhYDJ57jshU5DY6NudjIyIiQzBpomJLyujYREREUrF7jkySlDoljo5NRETGJClpioyMlLxDzj9HBU1qnRJHxyYiImOSlDR17NhR0s4UCgUyMjLyEw9RjnKrU1rSq4a2xakoj47NJ/qIiEyPpKQpMzOzoOMgkkRKnZIm+Siqo2PziT4iItPEmiYyKYbWKRW10bENaSkjIqKiJU9J08uXL3H06FHExMQgNVX3CaTRo0cbJTCirOSlTqkojY5tSEsZEREVLQYnTVFRUWjTpg0SExPx8uVLuLm5IS4uDnZ2dvDy8mLSRAWqKNcpScEn+oiITJfB4zSNGzcOYWFhePbsGVQqFU6ePIk7d+6gVq1a+PLLLwsiRiItTZ1SaJCHznK565Sk4hN9RESmy+CWpvPnz2PlypWwsLCApaUlUlJSULZsWXzxxRfo378/OnfuXBBxEmkVtTolQ5h6SxkRkTkzOGmytraGhcWrBiovLy/ExMQgJCQEzs7OuHv3rtEDJMpKYdYpGXN4gKL6RJ8cOOwCEZkag5OmGjVq4PTp0wgKCkLjxo0xbdo0xMXFYd26dahcuXJBxEgkm4IYHsCUW8qMhcMuEJEpMrim6fPPP4e3tzcAYNasWXB1dcXw4cPx+PFjrFq1yugBEsmlICf8dbazQTkvB1T3c0U5LwezSphyuq7Td1/Cw/hk3HyUgKiYZ7j5OIETKxNRkWFwS1Pt2rW1/+/l5YW9e/caNSCiooLDAxSM7K6rnY0letT1w4T/ncevN55ol7MFioiKCoNbmojMBYcHKBjZXdf3GwVg7YlbOgkTYJyWPSIiYzC4pSkgIAAKhSLb9X///bfkfc2ePRs7duzA1atXoVKp0KBBA8ydOxcVKlQwNCwioyuI4QFY/Jz9da3h64Klh25kuY4te0RUFBicNI0dO1bn57S0NERFRWHv3r348MMPDdrX0aNHER4ejjp16iA9PR0ff/wxWrZsicuXL8Pe3t7Q0IiMytjDA7D4+ZXsrmtKes5zXLJlj4jkZnDSNGbMmCyXf/311zhz5oxB+3qzHioiIgJeXl44e/YsQkNDDQ2NyKiMOTwA55z7V3bX1UXFgT+JqGgz2oS9rVu3xuTJk7F27do870OtVgMA3NzcjBUWUb4Ya3gAFpXryuq6OthaceBPIirSjJY0bdu2LV/JTmZmJsaOHYuGDRtmO95TSkoKUlJStD/Hx8fn+XhEUhljIE0WlevL6rpy4E8i4+D9smDkaXDL1wvBhRD4559/8PjxYyxbtizPgYSHh+PSpUs4fvx4ttvMnj0bM2bMyPMxiOTCOeek4cCfRMbB+2XBMDhp6tChg07SZGFhAU9PTzRp0gTBwcF5CmLkyJH48ccfcezYMZQuXTrb7SZPnozx48drf46Pj4evr2+ejklUmDjnnHSFOUUOUXHF+2XBMDhp+uSTT4x2cCEERo0ahZ07d+LIkSMICAjIcXulUgmlUmm04xMVFs45R0SFiffLgmFw0mRpaYnY2Fh4eXnpLH/y5Am8vLyQkZEheV/h4eHYuHEjdu/eDUdHR/zzzz8AAGdnZ6hU5vMINpkHdj0REZk2g5MmIUSWy1NSUmBjY9iH//LlywEATZo00Vm+du1aDBgwwNDQiIo8dj0REZkuyUnT4sWLAQAKhQLffvstHBwctOsyMjJw7Ngxg2uaskvAiIioYHBUeqK8k5w0ffXVVwBeJTorVqyApaWldp2NjQ3KlCmDFStWGD9CIiIyCo5KT5Q/kpOmW7duAQCaNm2KHTt2wNXVtcCCIiIi4+Ko9ET5Z3BN0+HDhwsiDiIiKkAclZ4o/ywMfUGXLl0wd+5cveVffPEFunXrZpSgiIjIuDgqPVH+GZw0HTt2DG3atNFb3rp1axw7dswoQREVFHViKm4+SkBUzDPcfJwAdWKq3CERFQqOSk+UfwZ3zyUkJGQ5tIC1tTXntqEijUWwZM44Kj1R/hnc0lSlShVs2bJFb/nmzZtRsWJFowRFZGy5FcGyxcm0mEOLobHPUTMqfWiQh85yjkpPJJ3BLU1Tp05F586dcfPmTbzzzjsAgIMHD2LTpk3YunWr0QMkMgYWwRYf5tBiWFDnyFHpifLH4JamsLAw7Nq1Czdu3MCIESPwn//8B/fu3cOBAwfQsWPHAgiRKP9YBFs8mEOLYUGfo7OdDcp5OaC6nyvKeTkwYSIygMEtTQDQtm1btG3bVm/5pUuXULly5XwHRWRsLIItHsyhxdAczpHIVBnc0vSmFy9eYNWqVahbty6qVatmjJiIjE5TBJsVFsGaDnNoMTSHcyQyVXlOmo4dO4Z+/frB29sbX375Jd555x2cPHnSmLERGQ2LYIsHc2gxNIdzJDJVBnXP/fPPP4iIiMDq1asRHx+P7t27IyUlBbt27eKTc1TksQjW9JnDY/PmcI5EpkpyS1NYWBgqVKiACxcuYOHChXjw4AGWLFlSkLERGR2LYE2bObQYmsM5EpkqyS1Ne/bswejRozF8+HAEBQUVZExERNkyhxZDczhHIlMkOWk6fvw4Vq9ejVq1aiEkJATvvfceevbsWZCxEZGJUSemIi4hFfHJaXBSWcPDvmBu9M52xT+BMIdzJDI1kpOm+vXro379+li4cCG2bNmCNWvWYPz48cjMzMT+/fvh6+sLR0fHgoyViIowcxh0ksjUxd25hrR0IcuxraysYK00/heB+NjbRt9ndhRCiDxfvejoaKxevRrr1q3D8+fP0aJFC0RGRhozvhzFx8fD2dkZarUaTk5OhXZcItKlTkzFyE1RWY4vFBrkgSW9arDVhEhGmvtlcaVS2eHq1Svw8/Mr0OPkK2nSyMjIwA8//IA1a9YwaSIyQzcfJaDZgqPZrj84vjHKeTkUYkRE9DrN/TKk9QA4efsX+vFfxsXiUuQqrF+/HiEhIUbfv4eHR4EnTEAeRwR/k6WlJTp27MhpVIjMFAdkJDINJSvVhVdQ9UI/7tOYaFyKXIWQkBDUrFmz0I9vLPkeEZyIiAMyEpE5YNJERPnGaWqIyBwwaSKifOOAjERkDoxS01QcFNb4MkTFFQdkJKLijkkTOL4MkbFwQEYiKs7MvntOnZiqlzABwLHrcZi0/QLUiakyRUZERERFidknTXEJqVkOyAe8SpziEpg0EREREbvnOL4MGR3r44iIiiezT5o4vgwZE+vjiIiKL7PvnuP4MmQsrI8jIirezD5p4vgyZCysjyMiKt7MvnsO4PgyZBysjyMiKt6YNP0/ji9D+cX6OCKi4s3su+eIjIX1cURExRuTJiIjYX0cEVHxxu45IiNifRwRUfHFpInIyFgfR0RUPLF7joiIiEgCJk1EREREEjBpIiIiIpKASRMRERGRBEyaiIiIiCTg03Nk1tSJqYhLSEV8chqcVNbwsOeTb0RElDUmTWS2HjxPwsTtF3Qm2Q0N8sCcLlXh46KSMTIiIiqK2D1HZkmdmKqXMAHAsetxmLT9AtSJqTJFRkRERRWTJjJLcQmpegmTxrHrcYhLYNJERES6mDSRWYpPTstx/Ytc1hMRkflh0kRmycnWOsf1jrmsJyIi88OkicySh4MNQoM8slwXGuQBDwc+QUdERLqYNJFZcrazwZwuVfUSp9AgD8ztUpXDDhARkR4OOUBFRmGPmeTjosKSXjUQl5CKF8lpcLS1hocDx2kiIqKsMWmiIkGuMZOc7ZgkERGRNOyeI9lxzCQiIjIFbGki2UkZMymn1iBOhUJERIWBSRPJLj9jJnEqFCIiKizsniPZ5XXMJHbrERFRYWLSRLLL65hJnAqFCpM6MRU3HyUgKuYZbj5OYFJOZIbYPUey04yZNGn7BRx7o5stpzGTOBUKFRZ2AxMRwKSJioi8jJnEqVCoMOTWDbykVw0+eEBkJpg0UZFh6JhJmm69Y1l00XEqFDKW/D7dSUTFB2uayGRxKhQqDOwGJiINtjSRSeNUKFTQ2A1MRBpMmsjkcSoUKkjsBiYiDXbPERHlgN3ARKTBliYiolywG5iIACZNRESSsBuYiNg9R0RERCQBkyYiIiIiCdg9R2QG1ImpiEtIRXxyGpxU1vCwZ1cTEZGhmDQRFXOcN42INOLuXENaush2vZWVFayVxv9CFR972+j7lINCCJH91Svi4uPj4ezsDLVaDScnJ7nDISpy1ImpGLkpKstpQEKDPDhvGpGZ0Nwv5aRS2eHq1Svw8/OTNY78YEsTUTHGedOI6HUhrQfAyds/y3Uv42JxKXIV1q9fj5CQEKMf28PDw6QTJqCIJE1ff/015s2bh3/++QfVqlXDkiVLULduXbnDIjJ5nDeNiF5XslJdeAVVz3Ld05hoXIpchZCQENSsWbNwAzMRsj89t2XLFowfPx7Tp0/HuXPnUK1aNbRq1QqPHj2SOzQik8d504iIjEf2pGnBggUYMmQIBg4ciIoVK2LFihWws7PDmjVr5A6NyORp5k3LCudNIyIyjKxJU2pqKs6ePYvmzZtrl1lYWKB58+b4/fff9bZPSUlBfHy8zj8iyh7nTSMyT7xfFgxZa5ri4uKQkZGBEiVK6CwvUaIErl69qrf97NmzMWPGjMIKj6hY4LxpROaH98uCIXv3nCEmT54MtVqt/Xf37l25QyIyCc52Nijn5YDqfq4o5+XAhImomOP9smDI2tLk4eEBS0tLPHz4UGf5w4cPUbJkSb3tlUollEplYYVHRERkkni/LBiytjTZ2NigVq1aOHjwoHZZZmYmDh48iLfeekvGyIiIiIh0yT5O0/jx49G/f3/Url0bdevWxcKFC/Hy5UsMHDhQ7tCIiIiItGRPmnr06IHHjx9j2rRp+Oeff1C9enXs3btXrziciIiISE6yJ00AMHLkSIwcOVLuMIiIiIiyZVJPzxERERHJhUkTERERkQRMmoiIiIgkYNJEREREJAGTJiIiIiIJmDQRERERScCkiYiIiEgCJk1EREREEjBpIiIiIpKASRMRERGRBEyaiIiIiCRg0kREREQkAZMmIiIiIgmYNBERERFJwKSJiIiISAImTUREREQSMGkiIiIikoBJExEREZEETJqIiIiIJGDSRERERCQBkyYiIiIiCZg0EREREUnApImIiIhIAiZNRERERBIwaSIiIiKSgEkTERERkQRMmoiIiIgkYNJEREREJAGTJiIiIiIJmDQRERERScCkiYiIiEgCJk1EREREEjBpIiIiIpKASRMRERGRBEyaiIiIiCRg0kREREQkgZXcARC9Tp2YiriEVMQnp8FJZQ0Pexs429nIHRYRERGTJio6HjxPwsTtF/Dr9TjtstAgD8zpUhU+LioZIyMiImL3HBUR6sRUvYQJAI5dj8Ok7RegTkyVKTIiIqJX2NJERUJcQqpewqRx7Hoc4hJS2U1HRJRP8f/EwEqZdct9fOztwg3GBDFpoiIhPjktx/UvcllPRES5O7t+To7rVSo7eHh4FFI0podJExUJTrbWOa53zGU9ERHl7ujRo3BwcMh2vYeHB/z8/AoxItPCpImKBA8HG4QGeeBYFl10oUEe8HBg1xwRUX5Vr14dTk5OcodhslgITkWCs50N5nSpitAg3Wbh0CAPzO1SlfVMREQkO7Y0UZHh46LCkl41EJeQihfJaXC0tYaHA8dpIiKiooFJExUpznZMkoiIqGhi9xwRERGRBEyaiIiIiCRg0kREREQkAZMmIiIiIgmYNBERERFJwKSJiIiISAImTUREREQSMGkiIiIikoBJExEREZEETJqIiIiIJGDSRERERCQBkyYiIiIiCUx6wl4hBAAgPj5e5kiIiIiMz9HREQqFQu4w6P+ZdNL04sULAICvr6/MkRARERmfWq2Gk5OT3GHQ/1MITXONCcrMzMSDBw/MPhOPj4+Hr68v7t69a9ZvLl6HV3gdXuF1eIXX4V+meC2MdX8TQuDFixdmf7/ML5NuabKwsEDp0qXlDqPIcHJyMpkPgoLE6/AKr8MrvA6v8Dr8yxyvhUKhMLtzLggsBCciIiKSgEkTERERkQRMmooBpVKJ6dOnQ6lUyh2KrHgdXuF1eIXX4RVeh3/xWlB+mXQhOBEREVFhYUsTERERkQRMmoiIiIgkYNJEREREJAGTJhN27NgxhIWFwcfHBwqFArt27ZI7JFnMnj0bderUgaOjI7y8vNCxY0dER0fLHVahW758OapWraodg+att97Cnj175A5LdnPmzIFCocDYsWPlDqVQffLJJ1AoFDr/goOD5Q5LFvfv30ffvn3h7u4OlUqFKlWq4MyZM3KHRSaISZMJe/nyJapVq4avv/5a7lBkdfToUYSHh+PkyZPYv38/0tLS0LJlS7x8+VLu0ApV6dKlMWfOHJw9exZnzpzBO++8gw4dOuCvv/6SOzTZnD59GitXrkTVqlXlDkUWlSpVQmxsrPbf8ePH5Q6p0D179gwNGzaEtbU19uzZg8uXL2P+/PlwdXWVOzQyQSY9Iri5a926NVq3bi13GLLbu3evzs8RERHw8vLC2bNnERoaKlNUhS8sLEzn51mzZmH58uU4efIkKlWqJFNU8klISECfPn3wzTffYObMmXKHIwsrKyuULFlS7jBkNXfuXPj6+mLt2rXaZQEBATJGRKaMLU1U7KjVagCAm5ubzJHIJyMjA5s3b8bLly/x1ltvyR2OLMLDw9G2bVs0b95c7lBkc/36dfj4+KBs2bLo06cPYmJi5A6p0EVGRqJ27dro1q0bvLy8UKNGDXzzzTdyh0Umii1NVKxkZmZi7NixaNiwISpXrix3OIXu4sWLeOutt5CcnAwHBwfs3LkTFStWlDusQrd582acO3cOp0+fljsU2dSrVw8RERGoUKECYmNjMWPGDLz99tu4dOkSHB0d5Q6v0Pz9999Yvnw5xo8fj48//hinT5/G6NGjYWNjg/79+8sdHpkYJk1UrISHh+PSpUtmWbsBABUqVMD58+ehVquxbds29O/fH0ePHjWrxOnu3bsYM2YM9u/fD1tbW7nDkc3rXfdVq1ZFvXr14O/vj//9738YNGiQjJEVrszMTNSuXRuff/45AKBGjRq4dOkSVqxYwaSJDMbuOSo2Ro4ciR9//BGHDx9G6dKl5Q5HFjY2NggMDEStWrUwe/ZsVKtWDYsWLZI7rEJ19uxZPHr0CDVr1oSVlRWsrKxw9OhRLF68GFZWVsjIyJA7RFm4uLigfPnyuHHjhtyhFCpvb2+9Lw0hISFm2VVJ+ceWJjJ5QgiMGjUKO3fuxJEjR1jk+ZrMzEykpKTIHUahatasGS5evKizbODAgQgODsbEiRNhaWkpU2TySkhIwM2bN/Hee+/JHUqhatiwod4QJNeuXYO/v79MEZEpY9JkwhISEnS+Nd66dQvnz5+Hm5sb/Pz8ZIyscIWHh2Pjxo3YvXs3HB0d8c8//wAAnJ2doVKpZI6u8EyePBmtW7eGn58fXrx4gY0bN+LIkSPYt2+f3KEVKkdHR716Nnt7e7i7u5tVnduECRMQFhYGf39/PHjwANOnT4elpSV69eold2iFaty4cWjQoAE+//xzdO/eHX/88QdWrVqFVatWyR0amSJBJuvw4cMCgN6//v37yx1aocrqGgAQa9eulTu0QvX+++8Lf39/YWNjIzw9PUWzZs3EL7/8IndYRULjxo3FmDFj5A6jUPXo0UN4e3sLGxsbUapUKdGjRw9x48YNucOSxQ8//CAqV64slEqlCA4OFqtWrZI7JDJRCiGEkClfIyIiIjIZLAQnIiIikoBJExEREZEETJqIiIiIJGDSRERERCQBkyYiIiIiCZg0EREREUnApImIiIhIAiZNRERERBIwaSIy0IABA9CxY0ftz02aNMHYsWMLPY4jR45AoVDg+fPn2W6jUCiwa9euQoupuLt9+zYUCgXOnz8vdyhEJAMmTVQsDBgwAAqFAgqFAjY2NggMDMSnn36K9PT0Aj/2jh078Nlnn0naVkqiY2o056RQKGBhYQFnZ2fUqFEDH330EWJjY/W2j4+Px5QpUxAcHAxbW1uULFkSzZs3x44dO6CZoOD136fm37vvvquzn6dPn6JPnz5wcnKCi4sLBg0ahISEhEI5ZyIyT5ywl4qNd999F2vXrkVKSgp+/vlnhIeHw9raGpMnT9bbNjU1FTY2NkY5rpubm1H2Y+qio6Ph5OSE+Ph4nDt3Dl988QVWr16NI0eOoEqVKgCA58+fo1GjRlCr1Zg5cybq1KkDKysrHD16FB999BHeeecduLi4APj396mhVCp1jtenTx/ExsZi//79SEtLw8CBAzF06FBs3Lix0M6ZiMwLW5qo2FAqlShZsiT8/f0xfPhwNG/eHJGRkQD+7VKbNWsWfHx8UKFCBQDA3bt30b17d7i4uMDNzQ0dOnTA7du3tfvMyMjA+PHj4eLiAnd3d3z00Ud4c7rGN7vnUlJSMHHiRPj6+kKpVCIwMBCrV6/G7du30bRpUwCAq6srFAoFBgwYAADIzMzE7NmzERAQAJVKhWrVqmHbtm06x/n5559Rvnx5qFQqNG3aVCdOqS5evIh33nkHKpUK7u7uGDp0qE7rTHp6OkaPHq0934kTJ6J///463ZHZ8fLyQsmSJVG+fHn07NkTJ06cgKenJ4YPH67d5uOPP8bt27dx6tQp9O/fHxUrVkT58uUxZMgQnD9/Hg4ODtptNb9PzT9XV1ftuitXrmDv3r349ttvUa9ePTRq1AhLlizB5s2b8eDBgyzj6927N3r06KGzLC0tDR4eHvj+++8BAHv37kWjRo2059+uXTvcvHkz23OOiIjQJnkau3btgkKh0Fm2e/du1KxZE7a2tihbtixmzJihbQUVQuCTTz6Bn58flEolfHx8MHr06ByuNBHJhUkTFVsqlQqpqananw8ePIjo6Gjs378fP/74I9LS0tCqVSs4Ojri119/xYkTJ+Dg4IB3331X+7r58+cjIiICa9aswfHjx/H06VPs3Lkzx+P269cPmzZtwuLFi3HlyhWsXLkSDg4O8PX1xfbt2wG8apWJjY3FokWLAACzZ8/G999/jxUrVuCvv/7CuHHj0LdvXxw9ehTAq+Suc+fOCAsLw/nz5zF48GBMmjTJoOvx8uVLtGrVCq6urjh9+jS2bt2KAwcOYOTIkdpt5s6diw0bNmDt2rU4ceIE4uPj81wTpVKpMGzYMJw4cQKPHj1CZmYmNm/ejD59+sDHx0dvewcHB1hZ/dv4feTIEXh5eaFChQoYPnw4njx5ol33+++/w8XFBbVr19Yua968OSwsLHDq1Kks4+nTpw9++OEHnSRx3759SExMRKdOnbTXaPz48Thz5gwOHjwICwsLdOrUCZmZmXm6BgDw66+/ol+/fhgzZgwuX76MlStXIiIiArNmzQIAbN++HV999RVWrlyJ69evY9euXdqWOSIqYgRRMdC/f3/RoUMHIYQQmZmZYv/+/UKpVIoJEyZo15coUUKkpKRoX7Nu3TpRoUIFkZmZqV2WkpIiVCqV2LdvnxBCCG9vb/HFF19o16elpYnSpUtrjyWEEI0bNxZjxowRQggRHR0tAIj9+/dnGefhw4cFAPHs2TPtsuTkZGFnZyd+++03nW0HDRokevXqJYQQYvLkyaJixYo66ydOnKi3rzcBEDt37hRCCLFq1Srh6uoqEhIStOt/+uknYWFhIf755x8hhBAlSpQQ8+bN065PT08Xfn5+Oucr5Zw09uzZIwCIU6dOiYcPHwoAYsGCBdnuS2PTpk1i9+7d4sKFC2Lnzp0iJCRE1KlTR6SnpwshhJg1a5YoX7683us8PT3FsmXLstxnWlqa8PDwEN9//712Wa9evUSPHj2yjePx48cCgLh48aIQQohbt24JACIqKkoIIcTatWuFs7Ozzmt27twpXv9obdasmfj88891tlm3bp3w9vYWQggxf/58Ub58eZGampptHERUNLCmiYqNH3/8EQ4ODkhLS0NmZiZ69+6NTz75RLu+SpUqOnVMf/75J27cuAFHR0ed/SQnJ+PmzZtQq9WIjY1FvXr1tOusrKxQu3ZtvS46jfPnz8PS0hKNGzeWHPeNGzeQmJiIFi1a6CxPTU1FjRo1ALzqjno9DgB46623JB9Ds49q1arB3t5eu6xhw4bIzMxEdHQ0bG1t8fDhQ9StW1e73tLSErVq1cpzS4vmOikUimyvWVZ69uyp/f8qVaqgatWqKFeuHI4cOYJmzZrlKRYrKyt0794dGzZswHvvvYeXL19i9+7d2Lx5s3ab69evY9q0aTh16hTi4uK05x0TE4PKlSvn6bh//vknTpw4oW1ZAl51+yYnJyMxMRHdunXDwoULUbZsWbz77rto06YNwsLCdFrdiKho4LuSio2mTZti+fLlsLGxgY+Pj95N5/VkAQASEhJQq1YtbNiwQW9fnp6eeYpBpVIZ/BpNd9FPP/2EUqVK6ax7s/jZ1Fy5cgUAUKZMGbi7u8PFxQVXr141eD9ly5aFh4cHbty4gWbNmqFkyZJ49OiRzjbp6el4+vQpSpYsme1++vTpg8aNG+PRo0fYv38/VCqVzlN5YWFh8Pf3xzfffAMfHx9kZmaicuXKOt28r7OwsNBLBtPS0nR+TkhIwIwZM9C5c2e919va2sLX1xfR0dE4cOAA9u/fjxEjRmDevHk4evQorK2tc702RFR4WNNExYa9vT0CAwPh5+cn6Vt6zZo1cf36dXh5eSEwMFDnn7OzM5ydneHt7a1TI5Oeno6zZ89mu88qVaogMzNTW4v0Jk1LV0ZGhnZZxYoVoVQqERMToxeHr68vACAkJAR//PGHzr5OnjyZ6zm+LiQkBH/++SdevnypXXbixAlYWFigQoUKcHZ2RokSJXD69Gnt+oyMDJw7d86g42gkJSVh1apVCA0NhaenJywsLNCzZ09s2LAhy2LthISEbIeIuHfvHp48eQJvb28Ar1rZnj9/rvO7OHToEDIzM/Va5F7XoEED+Pr6YsuWLdiwYQO6deumTUyePHmC6Oho/Pe//0WzZs0QEhKCZ8+e5XiOnp6eePHihc41fXMMp5o1ayI6OlrvdxsYGAgLi1cfwSqVCmFhYVi8eDGOHDmC33//HRcvXszx2EQkA3l7B4mM4/WaJqnrX758KYKCgkSTJk3EsWPHxN9//y0OHz4sRo0aJe7evSuEEGLOnDnCzc1N7Ny5U1y5ckUMGTJEODo6ZlvTJIQQAwYMEL6+vmLnzp3afW7ZskUIIcS9e/eEQqEQERER4tGjR+LFixdCCCGmTJki3N3dRUREhLhx44Y4e/asWLx4sYiIiBBCCHHnzh1hY2MjJkyYIK5evSo2bNggSpYsaVBN08uXL4W3t7fo0qWLuHjxojh06JAoW7as6N+/v3b7mTNnCnd3d7Fr1y5x9epVER4eLpycnETHjh2zPYampik6OlrExsaKa9euiU2bNokaNWoId3d38ddff2m3ffLkiQgODhalS5cW3333nfjrr7/EtWvXxOrVq0VgYKB49uyZePHihZgwYYL4/fffxa1bt8SBAwdEzZo1RVBQkEhOTtbu69133xU1atQQp06dEsePHxdBQUHaGrCcTJkyRVSsWFFYWVmJX3/9Vbs8IyNDuLu7i759+4rr16+LgwcPijp16uhcwzdrmp48eSLs7e3F6NGjxY0bN8SGDRuEj4+PTk3T3r17hZWVlfjkk0/EpUuXxOXLl8WmTZvElClThBCv6qK+/fZbcfHiRXHz5k3x3//+V6hUKhEXF5fruRBR4WLSRMVCXpImIYSIjY0V/fr1Ex4eHkKpVIqyZcuKIUOGCLVaLYR4VTw8ZswY4eTkJFxcXMT48eNFv379ckyakpKSxLhx44S3t7ewsbERgYGBYs2aNdr1n376qShZsqRQKBTahCUzM1MsXLhQVKhQQVhbWwtPT0/RqlUrcfToUe3rfvjhBxEYGCiUSqV4++23xZo1awxKmoQQ4sKFC6Jp06bC1tZWuLm5iSFDhmgTN835jhw5Ujg5OQlXV1cxceJE0a1bN9GzZ89sj6FJmgAIhUIhHB0dRbVq1cSHH34oYmNj9bZ//vy5mDRpkggKChI2NjaiRIkSonnz5mLnzp0iMzNTJCYmipYtWwpPT09hbW0t/P39xZAhQ7TF6hpPnjwRvXr1Eg4ODsLJyUkMHDhQ51yyc/nyZQFA+Pv76zwEIIQQ+/fvFyEhIUKpVIqqVauKI0eO5Jg0CfGq8DswMFCoVCrRrl07sWrVKvHm99G9e/eKBg0aCJVKJZycnETdunXFqlWrtK+vV6+ecHJyEvb29qJ+/friwIEDuZ4HERU+hRAGVGcSkVnJzMxESEgIunfvLnnUcyKi4oqF4ESkdefOHfzyyy9o3LgxUlJSsHTpUty6dQu9e/eWOzQiItmxEJyItCwsLBAREYE6deqgYcOGuHjxIg4cOICQkBC5QyMikh2754iIiIgkYEsTERERkQRMmoiIiIgkYNJEREREJAGTJiIiIiIJmDQRERERScCkiYiIiEgCJk1EREREEjBpIiIiIpKASRMRERGRBP8HCZhgUGflXOIAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "preds_and_labels = np.concatenate((test_scaffold.y.reshape([60, 1]), preds.reshape([60, 1])), axis=1)\n",
        "pred_df = pd.DataFrame(preds_and_labels, columns=['Actual log DC50 values', 'Predicted log DC50 values'])\n",
        "sns.jointplot(pred_df, x='Predicted log DC50 values', y='Actual log DC50 values')\n",
        "plt.annotate(f\"R: {eval_metrics['pearsonr']:.2f}\\nR²: {eval_metrics['pearson_r2_score']:.2f}\",\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             ha='left',\n",
        "             va='top',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round,pad=0.5', edgecolor='black', facecolor='white'))\n",
        "# Set the title\n",
        "plt.suptitle('Predicted vs Actual log DC50 Values from Scaffold Split')\n",
        "\n",
        "# Adjust the position of the title to avoid overlap with the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vagt1IG9oOXe"
      },
      "source": [
        "The model does significantly worse on the held out scaffold test set which was expected given the simplicity of the model. Developing far more complex models which can generalize out of distribution is a key area of focus in many areas of research from molecule property prediction to computer vision to natural language processing. In general, I hope this tutorial was a informative introduction into the world of PROTACs. Follow along as we explore how we can think about PROTAC design in the next tutorial!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QukfAL4UAzk"
      },
      "source": [
        "## 5. References\n",
        "\n",
        "<a name=\"1\"></a> [1] Kelm, J.M., Pandey, D.S., Malin, E. et al. PROTAC’ing oncoproteins: targeted protein degradation for cancer therapy. Mol Cancer. 2023, 22, 62. https://doi.org/10.1186/s12943-022-01707-5\n",
        "\n",
        "<a name=\"2\"></a> [2] Tu, Y., Chen, C., Pan, J., Xu, J., Zhou, Z. G., & Wang, C. Y. The Ubiquitin Proteasome Pathway (UPP) in the regulation of cell cycle control and DNA damage repair and its implication in tumorigenesis. International journal of clinical and experimental pathology. 2012, 5, 8.\n",
        "\n",
        "<a name=\"3\"></a> [3] Sun, X., Gao, H., Yang, Y. et al. PROTACs: great opportunities for academia and industry. Sig Transduct Target Ther. 2019, 4, 64. https://doi.org/10.1038/s41392-019-0101-6\n",
        "\n",
        "<a name=\"4\"></a> [4] Che Y, Gilbert AM, Shanmugasundaram V, Noe MC. Inducing protein-protein interactions with molecular glues. Bioorg Med Chem Lett. 2018, 28, 15. https://doi.org/10.1016/j.bmcl.2018.04.046.\n",
        "\n",
        "<a name=\"5\"></a> [5] Békés, M., Langley, D.R. & Crews, C.M. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov. 2022, 21, 181–200. https://doi.org/10.1038/s41573-021-00371-6\n",
        "\n",
        "<a name=\"6\"></a> [6] Liu, Z., Hu, M., Yang, Y. et al. An overview of PROTACs: a promising drug discovery paradigm. Mol Biomed. 2022, 3 (46). https://doi.org/10.1186/s43556-022-00112-0\n",
        "\n",
        "<a name=\"7\"></a> [7] Gaoqi Weng, Xuanyan Cai, Dongsheng Cao, Hongyan Du, Chao Shen, Yafeng Deng, Qiaojun He, Bo Yang, Dan Li, Tingjun Hou, PROTAC-DB 2.0: an updated database of PROTACs, Nucleic Acids Research. 2023, 51 (D1), Pages D1367–D1372, https://doi.org/10.1093/nar/gkac946\n",
        "\n",
        "<a name=\"8\"></a> [8] Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L. Computation of octanol-water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model. 2007, 47 (6), 2140-8. https://doi.org/10.1021/ci700257y.\n",
        "\n",
        "\n",
        "<a name=\"9\"></a> [9] Glem RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J. Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs. 2006, 9 (3).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GdF6Huajfai_"
      },
      "source": [
        "# **Congratulations! Time to join the Community!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXWnqWxZfhyZ"
      },
      "source": [
        "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLpv63D2fjNA"
      },
      "source": [
        "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
        "\n",
        "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12kjqoz9TUVr"
      },
      "source": [
        "## Join the DeepChem Discord\n",
        "\n",
        "The DeepChem [Discord](https://discord.gg/7yKPrJjR3T) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
      ]
    }
  ],
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